Art Therapy and Underlying Fmri Brain Patterns in Military Tbi
Med Sci (Basel). 2021 Mar; 9(1): 7.
Magnetoencephalography in the Detection and Characterization of Brain Abnormalities Associated with Traumatic Brain Injury: A Comprehensive Review
Geoffrey W. Peitz
aneDepartment of Neurosurgery, University of Texas Wellness San Antonio, San Antonio, TX 78229, United states; ude.ascshtu@ztieP
Ramesh Grandhi
3Section of Neurosurgery, Clinical Neurosciences Center, Academy of Utah, Salt Lake Metropolis, UT 84132, The states
Rodolfo Gabriel Gatto, Bookish Editor
Received 2020 Dec eighteen; Accepted 2021 January 29.
Abstruse
Magnetoencephalography (Million) is a functional brain imaging technique with loftier temporal resolution compared with techniques that rely on metabolic coupling. MEG has an important role in traumatic brain injury (TBI) inquiry, especially in mild TBI, which may not have detectable features in conventional, anatomical imaging techniques. This review addresses the original research articles to date that take reported on the employ of MEG in TBI. Specifically, the included studies have demonstrated the utility of MEG in the detection of TBI, characterization of brain connectivity abnormalities associated with TBI, correlation of brain signals with mail service-concussive symptoms, differentiation of TBI from post-traumatic stress disorder, and monitoring the response to TBI treatments. Although presently the utility of Million is mostly limited to research in TBI, a clinical office for Meg in TBI may become axiomatic with farther investigation.
Keywords: magnetoencephalography, traumatic brain injury, concussion, functional neuroimaging
1. Introduction
Traumatic brain injury (TBI) occurs an estimated 27 million to 69 meg times per twelvemonth throughout the globe [one,2]. The plurality of TBIs occur from unintentional falls, and about patients with TBI exercise not require hospitalization [3]. Nevertheless, even mild TBI may result in persistent symptoms such equally headache, cervicalgia, vertigo, and cognitive impairment, and the impairment may exist more pronounced with repeated TBI. Furthermore, balmy and sometimes moderate TBI is often undetectable via conventional imaging such as computerized tomography (CT) and anatomical magnetic resonance imaging (MRI). More than advanced imaging techniques are required for diagnosis, monitoring, and research of mild TBI.
Functional imaging techniques tin can be useful for diagnosis and prognosis in mild TBI and for monitoring the effects of therapeutic interventions [4,5]. Functional imaging has been shown to detect balmy TBI that is undetectable in conventional anatomic imaging, and long-term functional imaging abnormalities take been demonstrated in patients with chronic balmy TBI [6,vii,viii,9]. Functional imaging has likewise been used to analyze patterns of disrupted connectivity and the response to therapeutic interventions in severe TBI [10,xi].
Almost functional imaging techniques measure neuronal activeness indirectly through neurovascular or metabolic coupling, that is, to provide energy for increased neuronal activity, local cerebral blood menstruation, and glucose uptake increase. Neurovascular coupling is the basis for functional MRI (fMRI) with blood-oxygen-level-dependent (Assuming) imaging or arterial spin labeling (ASL) MRI. Neurovascular coupling is too the ground for single-photon emission computed tomography (SPECT), which measures gamma emissions from Technetium tc 99 1000 exametazime. Finally, neurovascular coupling is the basis for functional near infrared spectroscopy (fNIRS), which compares low-cal absorbance in the wavelengths captivated past oxyhemoglobin with the wavelengths captivated by deoxyhemoglobin. Alternatively, metabolism coupling to neuronal activity is the basis for positron emission tomography (PET), which detects emissions from a radiotracer linked to a biologically active molecule such equally fluorodeoxyglucose (FDG). Byrnes et al. have comprehensively reviewed the use of FDG-PET in TBI [12]. Although these techniques, especially fMRI, provide proficient spatial resolution, there is a filibuster between neuronal activation and the associated increase in blood menses and energy supply. Diagnostic techniques that directly measure neuronal activity, including electroencephalography (EEG) and magnetoencephalography (MEG), take superior temporal resolution.
1000000 measures magnetic flux on the surface of the head associated with underlying neuronal electrical currents. Magnetic signals from the brain were first recorded by the physicist David Cohen in 1968 [thirteen]. Since and then, One thousand thousand has developed into a sophisticated technique involving approximately 300 sensors on the scalp, forth with advisable shielding to minimize racket from groundwork magnetic fields. Ion currents from post-synaptic potentials make the biggest contribution to the MEG signal; the currents include intracellular currents along the soma–dendritic centrality and an contrary-direction extracellular return current [14]. To generate a magnetic field detectable by scalp sensors, simultaneous currents must occur in neurons with similar orientations. Therefore, the neocortical pyramidal neurons aligned perpendicular to the cortical surface generate the primary signals detected by 1000000, but activeness potentials and fast sodium ion spikes may likewise contribute if synchronized [fourteen]. Like EEG, Million allows for analysis of neural oscillatory activity across a range of frequency bands (e.g., delta, <4 Hz; theta, 4–viii Hz; alpha, viii–12 Hz; beta, 12–30 Hz; gamma, >thirty Hz). One thousand thousand has a similar temporal resolution to EEG, but MEG is less susceptible to distortion from variations in tissue conductivity than EEG [14]. EEG, however, can discover both radial and tangential currents, whereas MEG only detects the tangential component of currents. Therefore, Meg and EEG may be combined for optimal neuronal signal detection. Figure 1 demonstrates the orientation of electrical currents and magnetic fields in relation to Million and EEG sensors. Figure 2 demonstrates an overview of the 1000000 processing steps, including filtering external and physiological artifacts, identifying epochs and averaging signal from like epochs, and coregistration of the MEG sensor coordinates with anatomical MRI for source analysis.
(a) Pyramidal neuron with positive extracellular charge around the upmost dendrites and negative extracellular charge around the somatic dendrites, as in the case of a internet excitatory post-synaptic potential at the somatic dendrites and/or a net inhibitory post-synaptic potential at the apical dendrites. Purple arrow represents the extracellular electric current, and bluish arrows stand for the magnetic field associated with the extracellular current; (b) Cortical gyri and sulcus with pink arrows representing electric dipoles from synchronized neuronal action and blueish arrows showing the magnetic fields associated with these electric dipoles. The MEG magnetometer simply detects magnetic fields from dipoles oriented tangential to the cortical surface, whereas the EEG electrodes tin can find radial and tangential electric dipoles. Million = magnetoencephalography; EEG = electroencephalography.
Overview of the processing steps involved in magnetoencephalography from data acquisition to data analysis. MEG = magnetoencephalography; ECG = electrocardiogram; EOG = electrooculogram; MRI = magnetic resonance imaging.
Given the high incidence and prevalence of TBI and the demand for new technologies for the diagnosis, prognosis, and monitoring response to treatments in TBI, this review examines the electric current show for the use of 1000000 in TBI.
two. Literature Search
The authors conducted a PubMed search using the Medical Subject Headings (MeSH) "traumatic brain injury" and "magnetoencephalography" and the filter for homo studies. The manufactures were screened by reviewing their abstracts for relevance to clinical and enquiry applications of Million in adults with TBI. Articles were included in the review only if the full text was available in English.
In that location were 22 articles in PubMed plumbing equipment the search criteria. Screening the abstracts yielded xviii manufactures relevant to clinical and research applications of MEG in people with TBI. Ane of these manufactures was excluded because the full text was not available in English. From the reference lists of the articles found in the PubMed search, we identified xi additional original enquiry articles on the employ of MEG in TBI. Together, the articles described the utilise of One thousand thousand for detecting TBI, differentiating TBI from conditions with like features, characterizing changes in encephalon rhythm or connectivity from TBI, correlating imaging findings with clinical features, and monitoring the response to treatments. These applications are described in detail herein. Participant demographics for each study are shown in Table 1, and MEG arrangement and data analysis details are available in Tabular array two.
Table 1
Participant demographics for each study, listed in the lodge referenced. Sets are ranges unless otherwise specified with "mean." Fields marked with a hyphen indicate the information was not bachelor. "N/A" indicates "not applicable." * Retrospective review. ‡ Applies to affected patient grouping but. ^ Indicates years with post-traumatic stress disorder symptoms as opposed to years since traumatic brain injury.
| Report | No. of Affected Patients | No. of Salubrious Controls | % Female | Ages | Fourth dimension Since Injury |
|---|---|---|---|---|---|
| Lewine (1999) [15] | 30 | xx | 44 | xviii–57 | 2–16 months |
| Lewine (2007) [16] | xxx | N/A * | - | ≥xviii | ≥i year |
| Huang (2009) [17] | 10 | fourteen | 10 ‡ | 12–43 | 1–46 months |
| Huang (2012) [18] | 55 | 44 | 16 | Mean 27 ± eight | four weeks–iii years |
| Huang (2014) [nineteen] | 84 | 79 | 16 | Hateful 28 ± 9 | 4 weeks–v years |
| Zouridakis (2012) [twenty] | 10 | 10 | xxx | twenty–46 | >3 months |
| Vakorin (2016) [21] | 20 | 21 | 0 | 21–44 | <3 months |
| Dimitriadis (2015) [22] | 31 | 55 | 42 ‡ | Hateful 29 ± nine | <24 h |
| Kaltiainen (2018) [23] | 26 | 139 | 68 | 18–lx | six days–half dozen months |
| Li (2018) [24] | 13 | 8 | 48 | Mean 26 | - |
| Tormenti (2012) [25] | v | 5 | 50 | 16–57 | ≤4 months |
| Da Costa (2015) [26] | 16 | 16 | 0 | twenty–40 | 2 months |
| Popescu (2016) [27] | 32 | N/A | 0 | Mean xl | vi months–11 years |
| Rowland (2018) [28] | 18 | 10 | 0 | Mean 39 ± ten | Mean 6 ± 3 years |
| Zhang (2020) [29] | 23 | 21 | 0 | 18–48 | ane–4 years ^ |
| Luo (2013) [xxx] | 18 | eighteen | 0 | Mean 29 ± half-dozen ‡ | ≥six months |
| Dunkley (2015) [31] | twenty | 21 | 0 | Mean 31 ± 7 ‡ | <3 months |
| Antonakakis (2016) [32] | 30 | fifty | 43 ‡ | Hateful 29 ± 9 ‡ | <24 h |
| Antonakakis (2017) [33] | thirty | 50 | 43 ‡ | Mean 29 ± 9 | <24 h |
| Antonakakis (2020) [34] | 30 | l | 43 ‡ | Hateful 29 ± 9 | <24 h |
| Alhourani (2017) [35] | ix | 15 | 44 ‡ | 14–62 | 3 months–8 years |
| Dunkley (2018) [36] | 26 | 24 | 0 | Mean 31 ± seven ‡ | <3 months |
| Popescu (2017) [37] | 80 | N/A | 1 | Mean 59 | - |
| Li (2015) [38] | half dozen | 5 | 36 | Mean 29 ± 7 | - |
| Castellanos (2010) [39] | xv | fourteen | - | 18–51 | iv–6 months |
| Castellanos (2011) [twoscore] | 15 | xiv | 13 | 18–51 | 2–vi months |
| Lawton (2019) [41] | 4 | N/A | 0 | fifteen–68 | - |
| Huang (2017) [42] | 6 | N/A | 17 | 27–41 | Mean 48 ± 25 months |
Tabular array 2
Magnetoencephalography system and data assay details for each study, listed in the order referenced. MNI = Montreal Neurological Institute; SVM = back up vector car; CTF = CTF One thousand thousand International Services; ELM = extreme learning automobile; ANOVA = analysis of variance; AAL = Automated Anatomical Labelling atlas; LOOCV = leave-one-out cross-validation; AUR = area nether receiver operating characteristic curve; rRF = recursive random forests; g-NN = k nearest neighbors; ENS = ensemble classification; AEC = amplitude envelope correlation.
| Study | MEG Arrangement | Sensor/Source Space; Atlas, if Applicable | Functional Connectivity | Information Analysis | Features Selection if Machine Learning | Classifier if Motorcar Learning |
|---|---|---|---|---|---|---|
| Lewine (1999) [xv] | Magnes | Source | N/A | Z-score | North/A | Northward/A |
| Lewine (2007) [16] | Elekta | Source | N/A | Fisher verbal exam | N/A | North/A |
| Huang (2009) [17] | Elekta | Source | Due north/A | Nonparametric permutation tests | N/A | North/A |
| Huang (2012) [xviii] | Elekta | Source; MNI-152 | N/A | Correlation coefficient | N/A | N/A |
| Huang (2014) [nineteen] | Elekta | Source; MNI-152 | N/A | Z-score | North/A | Northward/A |
| Zouridakis (2012) [20] | Magnes | Sensor | Static | Machine learning | Fisher's benchmark ranking | SVM |
| Vakorin (2016) [21] | CTF | Source; AAL | Dynamic | Machine learning | LOOCV | SVM |
| Dimitriadis (2015) [22] | Magnes, Elekta | Sensor | Static | Machine learning | Tensor infinite dimensionality reduction | ELM |
| Kaltiainen (2018) [23] | Elekta | Source | N/A | Chi square | Northward/A | North/A |
| Li (2018) [24] | CTF | Source | Static | ANOVA | N/A | N/A |
| Tormenti (2012) [25] | Elekta | Source | N/A | Task-based activity, stepwise linear discriminant analysis | N/A | Northward/A |
| Da Costa (2015) [26] | CTF | Source | N/A | Task-based activity, t-examination, ANOVA | N/A | N/A |
| Popescu (2016) [27] | Elekta | Source; Desikan-Killiany | Northward/A | t-test, Mann–Whitney rank-sum, Spearman'southward rank correlation coefficient | N/A | North/A |
| Rowland (2018) [28] | CTF | Source | Static | Graph theory metrics, ANOVA | N/A | N/A |
| Zhang (2020) [29] | CTF | Source; AAL | Static | Machine learning, AUR | rRF | SVM |
| Luo (2013) [30] | Magnes | Sensor | N/A | Lempel-Ziv complexity, t-test | N/A | N/A |
| Dunkley (2015) [31] | CTF | Source; AAL | Static | AEC, nonparametric permutation tests | N/A | N/A |
| Antonakakis (2016) [32] | Magnes | Sensor | Static | Automobile learning | Tensor subspace analysis | thousand-NN, ENS, ELM |
| Antonakakis (2017) [33] | Magnes | Sensor | Static | Automobile learning | Iterative bootstrap | k-NN, SVM |
| Antonakakis (2020) [34] | Magnes | Source; AAL | Dynamic | Machine learning | Rank-feature | k-NN |
| Alhourani (2017) [35] | Elekta | Source; MNI-152 | Static | Stage synchrony, graph theory metrics | Due north/A | N/A |
| Dunkley (2018) [36] | CTF | Source | Static and dynamic | AEC, nonparametric permutation tests | N/A | N/A |
| Popescu (2017) [37] | Elekta | Source; Desikan-Killiany | N/A | Normalized evoked response power, ANOVA | N/A | N/A |
| Li (2015) [38] | CTF | Source; Desikan-Killiany | N/A | Z-score maps | N/A | N/A |
| Castellanos (2010) [39] | Magnes | Sensor | Dynamic | Distance-to-control connectivity patterns, Kruskal–Wallis | N/A | N/A |
| Castellanos (2011) [40] | Magnes | Sensor | Dynamic | Graph theory metrics, Kruskal–Wallis | Northward/A | Due north/A |
| Lawton (2019) [41] | Elekta | Source; MNI-152 | N/A | Task-based activity, t-exam | ||
| Huang (2017) [42] | Elekta | Sensor | N/A | Z-score maps | Northward/A | Northward/A |
3. Detection of TBI
Whereas most patients with severe TBI accept hemorrhage, edema, and/or ischemia detectable via CT or anatomical MRI, many balmy and sometimes moderate injuries do not have features visible with these conventional techniques. As with other functional imaging techniques, Meg has been shown to detect TBI that is otherwise silent in conventional imaging. 1000000 features indicating balmy TBI include abnormal resting state neural oscillations and altered connectivity between encephalon regions, as described in detail in this section.
Amidst the first to investigate MEG in TBI, Lewine et al. in 1999 compared conventional MRI, EEG, and resting-state Meg findings in 20 normal participants, xx symptomatic postal service-concussion patients, and 10 asymptomatic mail service-concussion patients [15]. The post-concussion patients' injuries occurred two–16 months prior to the study. Compared with MRI and EEG, Meg was significantly more sensitive for abnormalities in post-concussive patients, and all of the patients with MRI abnormalities also had MEG abnormalities. The MEG abnormalities were mostly characterized by abnormal low-frequency magnetic activity (ALFMA). Similarly, Lewine et al. (2007) retrospectively reviewed MRI, SPECT, resting-state Million, and neuropsychological testing findings in 30 patients with symptoms at least i year after mild blunt head trauma [16]. MEG was significantly more sensitive than MRI or SPECT for identification of abnormalities in patients with cerebral symptoms, and the notable MEG feature in this written report was abnormal dipole slow-wave activeness. Furthermore, the functional brain region in which the abnormalities occurred in each patient correlated with the patient'south specific cognitive symptoms (e.g., frontal lobe abnormal dipole boring-wave activity in patients with executive dysfunction). These studies set up the stage for 1000000 as an important tool in the diagnosis of mild TBI and in highlighting the anatomic correlation amid patients with even balmy functional deficits.
Farther studies take confirmed that resting-state 1000000 is sensitive for detecting balmy TBI. Huang et al. (2009) analyzed Million integration with diffusion tensor imaging (DTI) for detection of mild TBI in a case–control serial of 10 patients with subacute to chronic mild TBI and persistent mail-concussive symptoms for an average of 17 months and 14 matched controls [17]. Abnormal MEG slow waves were constitute in all 10 patients with mild TBI, whereas only seven patients with mild TBI had abnormalities in DTI and just one patient with mild TBI had abnormal conventional MRI findings. Furthermore, the grayness thing surface expanse generating Million slow waves was strongly correlated with the volume of non-major white matter tracts with reduced DTI anisotropy; major white matter tract injury was associated with much higher grayness matter surface area with slow waves. The authors theorized that pathologic tedious waves were a result of grey matter deafferentation from white matter tract injury, as has been shown in animal studies with EEG [43,44]. Afterward, in 2012, Huang et al. reported results from an automated abnormal low-frequency magnetic activeness (ALFMA) detection method in 45 patients with mild TBI and 10 patients with moderate TBI [eighteen]. Time from injury to the study ranged from 4 weeks to 3 years. ALFMA was detected in 87% of patients with mild TBI and 100% of patients with moderate TBI, and the number of cortical regions with ALFMA was significantly correlated with total mail-concussive symptom scores. Later on, Huang et al. (2014) developed voxel-based whole-encephalon MEG slow-moving ridge imaging and used information technology to compare 84 patients with persistent symptoms from subacute to chronic mild TBI (ranging between four weeks to 5 years post-injury) with 79 controls [19]. They reported an 84.five% detection rate for mild TBI (combining blast and not-boom injury mechanisms). Again, they found a correlation between the brain region with aberrant signal and the symptoms; MEG tedious waves in prefrontal areas correlated with personality change, difficulty concentrating, bear upon lability, and low.
Across comparing MEG rhythm abnormalities among specific brain regions, the connectivity between brain regions has also been used to detect TBI. Studies by Zouridakis et al. and Vakorin et al. used resting-land MEG network connectivity and car learning to detect mild TBI [20,21]. Patients in the Zouridakis et al. study sustained TBI more than than 3 months prior to the study, whereas patients in the Vakorin et al. study sustained TBI less than 3 months prior to the study. Both groups of researchers recorded resting-country One thousand thousand action in patients with balmy TBI and age- and sex-matched controls. Zouridakis et al. used Granger causality to appraise connectivity between brain regions, whereas Vakorin et al. used phase locking values (PLVs, values between 0 and one, representing the degree of phase synchrony between two brain regions) to assess connectivity. Both groups used back up vector machines to classify patients every bit having had a balmy TBI or not. The method used past Zouridakis et al. detected balmy TBI with 85% accurateness. The method used past Vakorin et al. detected balmy TBI with 88% accuracy, and the nomenclature confidence was correlated with TBI symptom severity scores. A unlike study published in 2015 by Dimitriadis et al. also used machine learning with PLVs from resting-state Meg data to allocate patients with acute, mild, or no TBI [22]. Instead of a support vector machine, they used the extreme learning machine classifier, and they reported 100% nomenclature accuracy. The findings of these investigators demonstrated that combining Meg connectivity analysis with machine learning tin can represent a powerful tool for detecting balmy TBI.
In another study of encephalon region connectivity with resting-state 1000000, Kaltiainen et al. included information from the same participants at multiple points in time. The authors analyzed resting-country 1000000 in 26 patients with mild TBI that occurred 6 days to half-dozen months prior to the study [23]. In 12 of the patients, follow-up 1000000 was washed half dozen months after. Of the 26 patients, vii had abnormal low-frequency MEG activity greater than ii standard deviations from the mean of that seen in 139 controls. At 6-month follow-upwards, however, only three of those seven patients had persistently abnormal depression-frequency Million activity. Li et al. (2018) similarly reported attenuation of Meg abnormalities over time in patients with mild TBI [24]. Using resting-state MEG signal source analysis and Granger causality to make up one's mind in-going and out-going connections between brain regions, they compared connectivity networks between thirteen patients with mild TBI and viii matched controls. There were significantly more stiff connections in the delta frequency band in patients with balmy TBI, just the divergence prodigal over iii successive visits for MEG recording. These studies suggest a temporal relationship for the presence of aberrant MEG signals and indicate a potential for MEG to be used for mild TBI diagnosis and likewise equally a radiological biomarker for encephalon "healing." This will be discussed further in the "Monitoring Response to Treatments" section of this review.
Beyond resting-state 1000000, task-based MEG has too proven useful in detecting TBI. Tormenti et al. published information from MEG recordings during a language and spatial task in five patients with a history of concussion inside the by 4 months and v controls [25]. The participants responded with whether each of a series of three figures containing geometric shapes matched the preceding sentence (e.k., "The blue square is below"). They pressed a button later on each word in the sentences and for each response on figures matching the sentences or not. The researchers recorded button response times and Million activity from nearly 100 brain recording sites per participant. They used statistical analyses to construct classification rules to categorize patients as concussed or not based on MEG data. Compared with controls, patients with previous concussions had significantly unlike normalized response times to the words and figures presented in the trials. Pairing One thousand thousand data from the occipitoparietal and temporal regions, the researchers synthetic a rule that correctly categorized eight of the 10 patients as concussed or not.
Da Costa et al. (2015) also used task-based Million in 16 patients with mild TBI inside 2 months of the study [26]. Patients with mild TBI showed delayed reaction times and different sequences of brain region activation compared with matched controls performing the same tasks.
In conclusion, both resting-country and task-based MEG take proven sensitive for detecting TBI even when conventional CT or MRI is normal. MEG may even exist more sensitive than other advanced imaging modalities such as SPECT. In the resting country, abnormal low-frequency MEG signals are associated with TBI, and the brain regions with the low-frequency signals correlate with the injury locations and symptoms. Moreover, One thousand thousand may be used as an adjunctive study for determining recovery from encephalon injury.
four. Differentiating Balmy TBI from Mail service-traumatic Stress Disorder
Whereas abnormal depression-frequency Meg activity may be sensitive for detecting TBI, it is not specific to TBI. Other neurologic and psychiatric conditions also feature abnormal low-frequency activity, including brain tumors, infarcts, epilepsy, Alzheimer illness, and schizophrenia [45]. Clinical history can diagnose epilepsy, and anatomical imaging such as CT and MRI tin can be used to diagnose brain tumors and infarcts. However, dementia and psychiatric disorders cannot e'er be differentiated from mild TBI using conventional diagnostic methods.
Contrasting brain abnormalities between mail-traumatic stress disorder (PTSD) and TBI is specially relevant because these weather condition are oftentimes comorbid, and balmy TBI may exacerbate PTSD symptoms [46,47,48]. Postconcussive symptoms, including anxiety or depression, apathy, changes in personality, dizziness, fatigue, headaches, irritability, and slumber disturbance, are commonly attributed to TBI, but the symptoms overlap with those of PTSD [49]. Clinically, it is important to distinguish between the 2 diagnoses because the natural history differs between psychological conditions and neurostructural damage. Therefore, attributing PTSD symptoms to TBI could give patients and providers the incorrect expectations for recovery [49]. Furthermore, the treatments for each condition are different. Dunkley (2015) theorized that the two disorders could be distinguished based on MEG and that increased high-frequency phase synchronization seen in PTSD could be the result of a psychological land, whereas increased depression-frequency amplitude coupling in mild TBI could be the outcome of neurostructural amending [50].
Building on this theory, Popescu et al. published a study in 2016 in which they analyzed resting-state One thousand thousand recordings in 32 agile-duty service-member patients with balmy TBI and persistent mail-concussive symptoms [27]. Each patient completed the PTSD Check Listing—War machine version (PCL-Grand); fifteen had low PTSD symptoms severity (PCL-M scores ≤44) and 17 had high PTSD symptom severity (PCL-M scores >44). To reduce the consequence of confounding psychological conditions, the researchers excluded patients with low and matched patients in the loftier and low PTSD symptoms severity groups for scores of generalized feet. They plant reduced One thousand thousand activity in the alpha frequency band in the dorsolateral prefrontal cortex of patients with high PTSD symptom severity compared with those with low PTSD symptom severity. The study showed that reduced alpha activity in the prefrontal cortex may exist a biomarker for PTSD in patients with mild TBI, merely information technology was non designed to elucidate the significance of the prefrontal alpha activity in the pathophysiology of PTSD in patients with TBI.
In another study investigating Meg activity in veterans with mild TBI and PTSD, Rowland et al. examined six veteran patients with both PTSD and chronic mild TBI, half-dozen patients with only chronic mild TBI, half-dozen patients with only PTSD, and 10 controls [28]. The participants completed the PCL-Thou. The researchers recorded resting-country 1000000 signals, then identified nodes of peak activeness in the brain. They measured phase consistency between each pair of nodes to create functional connectivity networks for each participant. Analyzing these networks, they found that patients with PTSD had decreased structure and increased randomness in their functional connectivity networks. Patients with balmy TBI had greater structure and less randomness. The authors' methods could exist useful in differentiating balmy TBI and PTSD, which are ofttimes comorbid in armed services patient groups too every bit intimate partner violence patient groups. However, as described in the Fifth Independent Medical Skillful Group report, further research is required before 1000000 tin be used as a diagnostic examination for TBI or PTSD [51].
In a step closer to using MEG clinically to diagnose PTSD, Zhang et al. (2020) developed a machine learning classifier to place military service members with PTSD [29]. They compared resting-state Meg information from 23 soldiers diagnosed with PTSD with those from 21 soldiers without PTSD but who had similar traumatic experiences in battleground deployment. The support vector machine (SVM) area under the receiver operating characteristic curve was 0.ix, demonstrating very good accuracy. However, this study did not directly look for TBI in conjunction with or as opposed to PTSD, so boosted research is still needed for MEG to distinguish TBI from PTSD accurately.
five. Characterizing Connectivity Abnormalities and Correlating with Clinical Features
Although detecting balmy TBI and differentiating it from other weather with similar features can be clinically important for tailoring existing treatment regimens, understanding the pathophysiology of persistent symptoms afterwards TBI is of import to developing and testing new handling modalities. Previous studies have used both resting-state and job-based EEG to study the issue of TBI on brain rhythm and connectivity, and MEG has also become useful for this [52,53,54,55]. As discussed in the previous sections, researchers have used 1000000 connectivity analyses with a focus on detecting and differentiating TBI [17,xx,21,22,23,24,25,26,27,28,l]. In this section, nosotros volition focus on discussing the use of Million connectivity analysis studies in exploring the pathophysiology of persistent symptoms afterwards TBI.
Luo et al. (2013) were amidst the first to evidence a correlation between MEG signal and specific cognitive symptoms in patients with TBI [xxx]. Using resting-state MEG data from xviii armed services veterans with known or suspected TBI at least 6 months prior to the study and eighteen age- and sex-matched controls, they applied Lempel–Ziv complication (LZC), which estimates complexity by the number of patterns in finite sequences. Participants were as well administered a number of neuropsychological tests, producing 44 neuropschological values representing attending, executive function, global cognitive part, language, memory, motor functions, processing speed, and visual–spatial skills. Not merely were there multiple brain regions with significantly lower LZC in patients with TBI compared with controls, but at that place were also 4 neuropsychological values that significantly correlated with LZC in singled-out brain regions. The functional significance of positive vs. negative correlations between LZC and neuropsychological values requires further investigation, but the results from this study advise that LZC applied to MEG may be a tool for analyzing motor, reasoning, and visual perception dysfunction later TBI.
Equally described in the section on TBI detection, Dimitriadis et al. (2015) analyzed Meg connectivity based on PLVs with machine learning to find balmy TBI with high accuracy, only they as well used graph theory to better characterize the different regional brain network connectivity between patients with balmy TBI and age-matched controls [22]. The control participants had strong local connections and some long-range connections that accounted for 20% of the total number of identified connections. Conversely, patients with balmy TBI had weak local connections only strong long-range connections that accounted for lx% of the total. Furthermore, the long-range connections in the controls mainly linked frontal to fundamental regions or primal to peripheral regions, but the long-range connections in patients with mild TBI mainly linked peripheral regions. Dunkley et al. (2015) and Antonakakis et al. (2016, 2017) also showed differences in encephalon connectivity across multiple frequency bands between patients with mild TBI and controls [31,32,33].
In 2020, Antonakakis et al. studied spontaneous network microstates in patients with TBI [34]. Using resting-state One thousand thousand information from xxx patients with balmy TBI and 50 healthy controls, they synthetic dynamic functional connectivity graphs from PLVs. Subsequently, they used a vector quantization process to compute network microstates for each participant and analyzed how these network microstates changed over fourth dimension segments. So, using a machine learning classifier with network microstate features, they reported 94% accuracy in classifying patients with mild TBI and healthy controls. The patients with mild TBI had lower time-resolved arrangement in their encephalon connectivity networks compared with controls.
A written report by Alhourani et al. (2017) likewise used PLVs from resting-state Million data to characterize functional connectivity in patients with chronic, mild TBI (3–96 months after injury) and persistent post-concussive symptoms [35]. Using graph theory, they institute that local communication efficiency was reduced in all frequency bands in patients with balmy TBI compared with controls. The researchers too reported reduced connectivity predominantly in the parietal and occipital lobes of patients with mild TBI compared with controls. They noted that specific regions with significantly reduced connectivity included major hubs in the default mode network (DMN), a network active during rest and associated with memory and attending [35]. In 2018, Dunkley et al. expanded on the significance of the DMN in TBI [36]. The researchers obtained resting-state MEG recordings in 20 patients with a concussion within 3 months prior to the written report and twenty age- and sex-matched controls. The participants as well underwent cognitive behavioral testing including the Sports Concussion Assessment Tool 2 (SCAT-2). The researchers calculated amplitude envelope correlations (AECs), which are values measuring the temporal correlation between waveform amplitudes in separate brain regions, independent of their phases. They used the AECs to construct graphs of intrinsic connectivity networks. As opposed to Alhourani et al. (2017), they constitute increased DMN connectivity in the blastoff and beta frequency bands in patients with previous concussions. Furthermore, after controlling for comorbidities, the authors found that DMN connectivity had a meaning, positive correlation with concussion symptoms.
Some other study correlating post-concussion symptoms with brain connectivity findings was published past Popescu et al. in 2017 [37]. Rather than using a full general symptom scale score, these authors focused on word finding difficulty associated with mild TBI. They recorded task-based Million information in 57 right-handed military service members with a history of balmy TBI and persistent symptoms who were stratified into three cognitive functioning groups based on the Rivermead Behavioral Retention Exam. During One thousand thousand recording, the participants were presented with 80 pictures of mutual objects and tasked with naming them. The authors reported early activation of widely distributed networks for visual and linguistic processing in the ascendant hemisphere after presentation with an object to be named. They also found widespread decreased amplitude of the response in patients with mild TBI and depression cerebral operation scores compared with those with mild TBI and medium or loftier cognitive performance scores.
Finally, Meg can also be combined with EEG to characterize activity in the brain associated with TBI. In 2015, Li et al. published a study in which they analyzed resting-state Meg and EEG activity at 68 encephalon regions of interest [38]. Comparing brain activation maps between patients with mild TBI and controls, they found significant differences in low-frequency action on both EEG and Million. The sample size was limited to 6 patients with mild TBI and v controls, only further research combining EEG and One thousand thousand may bear witness useful in characterizing detailed rhythm and connectivity abnormalities later TBI and explaining how these abnormalities relate to symptoms.
6. Monitoring Response to Treatments
Multiple studies have shown that MEG abnormalities in TBI dissipate with fourth dimension since the injury and/or with recovery from the injury [23,24,56], and others accept demonstrated that MEG abnormalities correlate with symptoms severity [18,21]. Further studies take taken an boosted footstep past incorporating handling programs into the enquiry protocol and using MEG to look for responses to the treatments. This is an important surface area of research that could have significant clinical implications.
In early on studies on response to TBI treatment, Castellanos et al. (2010) compared resting-land MEG recordings in patients with chronic TBI (range 2–6 months later injury) earlier and afterwards a neuropsychological rehabilitation program [39,forty]. The patients had severe cognitive damage, and the rehabilitation programs involved cerebral therapy and lasted seven–12 months. The authors used graph theory to analyze One thousand thousand information and found that, compared with controls, patients with TBI had increased network force in the delta frequency ring and decreased network strength plus network reorganization in the blastoff frequency band [39]. After rehabilitation, the network characteristics in each frequency band became more like to those of the control participants. In 2011, Castellanos et al. reported similar network changes in patients pre- and postal service-rehabilitation for chronic TBI (range 4–6 months after injury) but also institute correlations between region-specific connectivity values and neuropsychological examination scores for various cerebral functions. These studies identified parameters that are correlated with pre- and post-rehabilitation status in TBI patients, but they were non designed to determine whether fourth dimension since injury or the rehabilitation plan was responsible for the 1000000 changes and cognitive recovery.
MEG has also been used to evaluate a more specific therapeutic intervention, perception attention therapy (PATH), which is a visual timing training task involving movement discrimination. Lawton and Huang (2019) studied 4 patients with cognitive deficits attributed to TBI who underwent working memory task-based MEG before and afterward eight weeks of PATH neurotraining [41]. After the intervention, the Meg action during the working retentivity job increased in the participants' frontoparietal attention networks and dorsal stream, every bit did their performance on standardized tests of attention, reading, and working memory skills.
Besides rehabilitation programs requiring active participation, a passive TBI handling intervention has also been tested with MEG. Huang et al. (2017) recorded MEG data in patients with chronic TBI and persistent mail-concussive symptoms (average duration 48 ± 25 months) earlier and after a half-dozen-calendar week passive neurofeedback intervention using depression-intensity pulse transcranial electric stimulation with EEG monitoring [42]. They reported reductions in both abnormal Meg slow waves and post-concussive symptom scores after the intervention. Similarly to the studies by Castellanos et al., this study lacked a cypher/sham intervention group and then whether the intervention or the timing was responsible for the changes is unclear. However, both inquiry groups have shown that MEG tin be used as a supplement to neuropsychological testing to assess recovery after TBI and that MEG parameters could be used equally outcome measures for future studies assessing the efficacy of TBI clinical treatment interventions.
7. Limitations of MEG
Although the temporal resolution of Million is very good, its spatial resolution is inferior to that of MRI. For functional brain activity maps, Million data are often co-registered with anatomical MRI for Meg source imaging, requiring patients/participants to undergo an MRI scan in improver to the 1000000 data acquisition session. However, even with MRI-aided source modelling, Kaltiainen et al. (2018) demonstrated limited sensitivity of MEG to traumatic lesions deep in the brain (>three cm from the cortex) [23]. Therefore, functional imaging methods with stiff spatial resolution such as fMRI are important complements to Million in TBI research. Additionally, longitudinal studies that assess the progression of Meg symptom abnormalities and the relation of these signals to cognitive and other outcome over time are quite limited; this was identified equally a major gap in the clinical utility of MEG in TBI in a consensus panel height convened by the United Kingdom Office of the Surgeon General in 2020 to specifically accost the relative utility of 1000000 and other imaging techniques [57].
MEG machines are as well very expensive and require a magnetically shielded room. Specialized expertise is also required for analysis and interpretation. Thus, it is not available in many institutions. The benefit of 1000000 every bit an adjunct in choosing depth electrode sites for epilepsy surgery planning may be worth the additional cost [58]. Notwithstanding, Meg does not yet take a concrete clinical application in TBI with a price–do good ratio proven favorable.
eight. Conclusions
MEG is a functional brain imaging technique with loftier temporal resolution and reasonable spatial resolution when co-registered with anatomical MRI. TBI researchers accept demonstrated a variety of uses for Million, especially in the detection of mild TBI and the characterization of functional network connectivity changes from TBI that cannot be seen with conventional anatomical imaging techniques. Combining MEG with other techniques, such as EEG or DTI, can support efforts to understand connectivity changes from TBI. MEG research has also been directed toward differentiating TBI from other weather condition with similar clinical features, such as PTSD, every bit well as toward assessing responses to TBI handling interventions. Although presently the utility of One thousand thousand is mostly limited to research in TBI, future research may somewhen identify a clinical role for information technology in TBI every bit it has in epilepsy.
Acknowledgments
The authors would like to admit Jennie Williams for medical illustrations and Kristin Kraus for medical editing.
Author Contributions
Conceptualization, Thousand.W.P., R.G. and E.A.W.; literature search, Chiliad.W.P. and Due east.A.Due west.; writing—original draft preparation, G.Westward.P.; writing—review and editing, R.Grand. and E.A.W. All authors have read and agreed to the published version of the manuscript.
Funding
This research received no external funding.
Institutional Review Board Statement
Not applicable.
Informed Consent Statement
Not applicable.
Data Availability Statement
No new information were created or analyzed in this study. Data sharing is non applicable to this article.
Conflicts of Interest
The authors declare no disharmonize of interest.
Footnotes
Publisher'due south Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.
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