Abstract
Advances in neuroimaging, complementing histopathological insights, have established a multi-system involvement of cerebral networks beyond the traditional neuromuscular pathological view of amyotrophic lateral sclerosis (ALS). The development of effective disease-modifying therapy remains a priority and this will be facilitated by improved biomarkers of motor system integrity against which to assess the efficacy of candidate drugs. Functional MRI (FMRI) is an established measure of both cerebral activity and connectivity, but there is an increasing recognition of neuronal oscillations in facilitating long-distance communication across the cortical surface. Such dynamic synchronization vastly expands the connectivity foundations defined by traditional neuronal architecture. This review considers the unique pathogenic insights afforded by the capture of cerebral disease activity in ALS using FMRI and encephalography.
Keywords: amyotrophic lateral sclerosis, motor neurone disease, biomarker, neuroimaging, neurophysiology, cortex
Introduction
Neurodegenerative disorders are increasingly understood as a disintegration of complex cerebral functional networks (1). Amyotrophic lateral sclerosis (ALS) is characterized by loss of upper and lower motor neurones of the corticospinal tract, brainstem, and spinal anterior horns, resulting in progressive weakness of downstream muscles. In addition to protean upstream causes (2, 3), there is firmly established clinical, pathological, and genetic overlap of ALS with frontotemporal dementia (FTD). The diagnosis remains a clinical one, with a lack of biomarkers being a significant barrier to the development of highly-effective disease-modifying therapy. Advanced neuroimaging, in combination with histopathological insights, has brought the brain to the forefront of biomarker development (4).
The earliest studies of cerebral blood flow in ALS employed positron emission tomography, and demonstrated a widened region of cortical activation in response to a simple motor task (5). Among the hypotheses for this “boundary shift effect” was loss of local inhibitory GABA-ergic interneuronal circuits [reviewed in (6)]. A consistent pathological feature of ALS has been the observation of increased cortical excitability, possibly reflecting reduced local inhibitory influences, measured using short-interval paired transcranial magnetic stimulation (TMS) (7, 8). Through the characterization of monogenetic associations, ALS research has expanded to include the study of what is now thought to be a long presymptomatic phase (9), in which cortical functional abnormalities may be the among the earliest detectable manifestations (10).
Blood oxygenation level-dependent (BOLD)-based functional (FMRI), with the major advantages of avoiding ionizing radiation and the greater availability of MRI technology, confirmed a profound alteration in cortical activity inherent to the pathogenesis of ALS. Tools to study cortical neurophysiology in real-time have also undergone significant development in both sensitivity and analysis. This review will consider the unique insights that FMRI and encephalography bring to the understanding of the pathogenesis of ALS at the systems level, which is increasingly of greatest relevance to the long-term goal of neuroprotective therapy.
Functional MRI
FMRI has been extensively used to characterize network dysfunction in ALS in cross-sectional, longitudinal, and presymptomatic study designs. The practical advantages of FMRI in ALS include the widespread availability of MRI platforms, the large number of freely available analysis suites and the ability to provide crucial biological insights in relatively simple, cost-effective, and non-invasive study designs (11). Despite some inconsistencies in the literature (12), two main themes have consistently emerged from the application of FMRI to ALS: (i) the characterization of compensatory changes, such as the recruitment of additional cortical (13–16), subcortical (14, 17, 18), and cerebellar (17, 19) regions to execute motor and cognitive tasks, and ii) the concept of inhibitory dysfunction as a key facet of ALS-associated pathophysiology (6, 20).
Methodological Considerations
Functional MRI has been extensively used to characterize network dysfunction in ALS in cross-sectional, longitudinal, and presymptomatic study designs (21) (Table 1), but method-associated limitations are rarely articulated. The protracted hemodynamic response to neural activity hampers the temporal resolution of fMRI. Blood oxygen-level dependent (BOLD) signal typically peaks only 5–6s after focal activation therefore careful study designs are indispensable for meaningful temporal inferences (22, 23). Spatial distortions and signal dropout due to susceptibility gradients near air-tissue interfaces lead to decreased BOLD signal in lateral parietal, orbitofrontal and dorsolateral prefrontal regions necessitating meticulous sequence optimization before data acquisition (24–26). Scanner noise may also impact on the interpretation of BOLD signal, particularly in the default-mode network (27), and careful experimental designs are required to minimize the influence of background noise (28). While fMRI findings are often presented by overlaying activation maps upon high-resolution structural images, the inherent spatial resolution of fMRI is limited by the signal-to-noise ratio profile of consecutive, rapid whole-brain imaging. In the majority ALS studies, the voxel size of fMRI protocols is two to four times larger than what is used for structural acquisitions (21).
Table 1.
Authors | Year | Study design | ALS (n) | Controls (n) | Main study findings/interpretation |
---|---|---|---|---|---|
Li et al. (29) | 2009 | Motor task: swallowing, Cross-sectional study |
10 | 10 HC | Reduced somatosensory cortex activation in patients with dysphagia |
Mohammadi et al. (30) | 2009 | Motor: tongue movement, Cross-sectional study |
22 | 22 HC 5 DC (SBMA) |
ALS patients with bulbar symptoms showed decreased cortical and thalamic activation |
Palmieri et al. (53) | 2010 | Emotional attribution and recognition task, Cross-sectional study |
9 | 10 HC | Altered emotional processing similar to patents observed in FTD. |
Lule et al. (55) | 2010 | visual, auditory and somatosensory stimulation, Cross-sectional study |
14 | 18 HC | Decreased response in secondary visual areas in ALS, delayed response in secondary auditory areas, reduced response to somatosensory stimulation |
Goldstein et al. (52) | 2011 | Cognitive task, Cross-sectional study |
14 | 8 HC | Increased left temporal and decreased precentral and left medial frontal activation: altered inhibitory processing in ALS |
Kollewe et al. (13) | 2011 | Motor task: hand and tongue movement, Cross-sectional study |
20 | 20 HC | Decreased cortical activation during tongue movements in patients with bulbar symptoms. Increased activation during hand movements. Different functional reorganization in limb and bulbar impairment. |
Mohammadi et al. (14) | 2011 | Motor task, Cross-sectional study |
22 | 22 HC | Patients stratified into three groups based on disability, Increased activation in early-stage, decreased activation in later stage disease. |
Poujois et al. (15) | 2013 | Hand motor task Motor imagery, Cross-sectional study |
19 | 13 HC | Motor execution and imagery yields to similar activation patterns. Increased contra- and ipsilateral somatosensory cortex activation. |
Passamonti (59) | 2013 | Emotional processing task, Cross-sectional study | 11 | 12 HC | Increased activation in prefrontal areas and altered amygdala-prefrontal cortex connectivity in ALS, suggestive of limbic system dysfunction |
Witiuk et al. (57) | 2014 | Antisaccade task with eye tracking, Cross-sectional study |
12 | 12 HC | ALS patients make more antisaccade direction errors and exhibit reduced DLPFC activation compared to controls i.e. deficits in automatic response inhibition are associated with impaired DLPFC activation |
Stoppel et al. (43) | 2014 | Go/No-Go paradigm, Longitudinal design |
14 | 14 HC | Increased motor activation compared to controls with subsequent decline on follow-up scanning suggestive of failing adaptive compensation |
Mohammadi et al. (61) | 2015 | Movement inhibition task (go/no-go), Cross-sectional study |
17 | 17 HC | Increased motor inhibition and execution related activation in patients with ALS compared to controls. |
Jelsone-Swain et al. (36) | 2015 | Action Observation and Execution task, Cross-sectional study |
19 | 18 HC | Increased activation during action-execution and observation in ALS patients in opercular, premotor and primary motor regions. Mirror neuron system mediated compensation. |
Li et al. (35) | 2015 | Action observation paradigm, Cross-sectional study |
30 | 30 HC | Action observation activates similar networks to action execution. Increased activation observed in the DLPFC and supplementary motor regions of ALS patients. |
Aho-Ozhan (60) | 2016 | Cognitive task Cross-sectional study |
15 | 14 HC | Impaired processing of negative emotions such as fear and disgust in ALS |
Vellage et al. (54) | 2016 | Cognitive task: working memory Cross-sectional study |
14 | 14 HC | Reduced hemodynamic responses in the left occipital cortex and right prefrontal cortex in ALS patients compared to healthy controls |
Keller et al. (58) | 2018 | Cognitive task: ToM and executive task, Cross-sectional study |
65 | 33 | Increased activation in all ALS patients compared to HC. High performing patients exhibit more activation than those with neuropsychological deficits suggestive of compensation. |
Mohammadi et al. (66) | 2009 | Resting-state fMRI, Cross-sectional study |
20 | 20 HC | ICA analyses: decreased DMN activation in the anterior and posterior cingulate and parietal regions |
Jelsone-Swain et al. (84) | 2010 | Resting-state fMRI, Cross-sectional study |
20 | 20 HC | ROI analyses: decreased functional connectivity between the right and left motor cortices |
Douaud et al. (20) | 2011 | Resting-state fMRI, Cross-sectional study |
25 | 15 HC | Increased SMN, premotor, prefrontal and thalamic functional connectivity, interpreted as compensation and inhibitory dysfunction |
Agosta et al. (42) | 2011 | Resting-state fMRI, Cross-sectional study |
26 | 15 HC | Increased SMN, cingulate, cerebellar connectivity interpreted as compensation. |
Fekete et al. (76) | 2013 | Resting state fMRI, Cross-sectional study |
40 | 30 HC | Widespread motor, cerebellar and basal ganglia functional connectivity alterations in the ALS cohort. Accurate subject classification using multiple kernel learning. |
Zhou et al. (82) | 2013 | Resting-state fMRI, Cross-sectional study |
12 | 12 HC | Positive correlation between disability and functional connectivity |
Agosta et al. (65) | 2013 | Resting-state fMRI, Cross-sectional study |
20 | 15 HC | ICA analyses: Increased parietal connectivity is associated with cognitive deficits which may represent compensation |
Welsh et al. (67) | 2013 | Resting state fMRI, Cross-sectional study |
32 | 31 HC | Machine learning (support-vector machine) based on fMRI metrics achieves over 71% accuracy for disease state classification |
Zhou et al. (73) | 2014 | Resting state fMRI, Cross-sectional study |
12 | 12 HC | Decreased regional brain synchrony in the superior medial SMN detected by regional coherence measures |
Meoded et al. (38) | 2015 | Resting state fMRI, Cross-sectional study |
14 HC 16 PLS |
Increased functional connectivity between the cerebellum and cortical motor areas and between the cerebellum and frontal and temporal cortex in primary lateral sclerosis | |
Schmidt et al. (77) | 2014 | Resting state fMRI, Cross-sectional study |
64 | 27 HC | A strong positive correlation exist between changes in SC and FC averaged per brain region; suggesting that structural and functional network degeneration in ALS is coupled |
Chenji et al. (75) | 2016 | Resting state fMRI, Cross-sectional study |
21 | 40 HC | Increased DMN and reduced SMN connectivity associated with greater disability interpreted as inhibitory dysfunction |
Zhou et al. (69) | 2016 | Resting state fMRI, Cross-sectional study |
44 | 44 HC | Increased cerebellar, occipital and prefrontal degree centrality (DC) and decreased DC in the primary motor cortex and sensory motor regions of ALS patients |
Menke et al. (79) | 2016 | Resting state fMRI, Presymptomatic study design |
12 | 12 psALS 12 HC |
Increased FC between the cerebellum and precuneus- cingulate-frontal lobe network in asymptomatic mutation carriers compared to controls |
Trojsi et al. (72) | 2017 | Resting state fMRI, Cross-sectional study |
21 | 15 | Decreased FC in DMN, salience and fronto-parietal network. More significant SLN connectivity changes observed in bulbar onset patients compared to those with spinal onset. |
Zhang et al. (74) | 2017 | Resting state fMRI, Cross-sectional study |
38 | 35 HC | Impaired interhemispheric functional connectivity eidenceed by voxel mirrored homotopic connectivity (VMHC) reductions, correlations with CC diffusivity metrics |
Zhang et al. (166) | 2017 | Resting state fMRI, Cross-sectional study |
25 | 25 HC | Reduced occipital surface-based local gyrification index (LGI) is associated with decreased functional connectivity in the bilateral precuneus. |
Lee et al. (80) | 2017 | Resting state fMRI, Presymptomatic study design |
13 psALS 46 HC |
Connectivity deficits detected in salience, sensorimotor, default mode and thalamic networks in presymptomatic C9orf72 carriers | |
Li et al. (68) | 2018 | Resting state fMRI, Cross-sectional study |
38 | 35 HC | Graph theory method (functional connectivity density FCD) Decreased FCD in the primary motor cortex, increased long-range FCD in the premotor cortex in ALS patients. |
Bueno et al. (167) | 2018 | Resting state fMRI, Cross-sectional study |
20 | 15 HC | Focus on Papez circuit integrity. Decreased functional connectivity in ALS patients between hippocampal, parahippocampal and cingulate regions. |
Menke et al. (39) | 2018 | Resting state fMRI, Longitudinal study |
13 | 3 PLS | Multi-timepoint structural-functional study, ICA and DRA, decreased FC between SMN and frontal pole, increased FC between primary motor cortex and fronto-parietal network |
HC healthy control, DC Disease Control, DLPFC DorsoLateral PreFrontal Cortex, FC functional connectivity, SC Structural Connectivity, SBMA Kennedy's disease, FTD FrontoTemporal Dementia, PLS Primary Lateral Sclerosis, DMN Default Mode Network, SMN SensoriMotor Network, ToM Theory of Mind.
Motor Paradigms
Pioneering FMRI studies in ALS relied initially on hand movement paradigms (15, 16), which were gradually complemented by innovative bulbar studies (13, 29, 30). In motor-task FMRI studies, different strategies have been utilized to control for limb weakness, motor effort and lower motor neuron involvement for the interpretation of cerebral activation. Motor imagery (31) has attracted considerable attention, not only for emerging brain-machine interface applications (32) but also as an FMRI paradigm for a condition like ALS in which patients typically develop severe motor disability (33). The execution and imagination of specific movements manifest in similar activation patterns in ALS and controls (15) suggesting that this approach may be particularly pertinent to patient cohorts with mixed disability profiles. Some ALS studies however report divergent activation maps in motor imagery and execution (34). Similarly to motor imagery, action observation is also thought to result in comparable cortical activity to action execution which has been used to study the mirror-neuron system in ALS (35, 36). Another approach to control for motor disability and establish ALS-specific activation patterns is the inclusion of disease-controls, i.e., non-ALS patients with motor disability (16, 37). Very few FMRI studies to date have specifically evaluated functional changes in other rarer motor neurone disorders such as the upper motor neurone-only primary lateral sclerosis (PLS) (38, 39) and lower motor neurone-dominated Kennedy's disease (30) using motor paradigms. Patient stratification into separate study groups based on motor disability is another strategy to interpret functional alterations in the context of disability (14). In light of the fundamentally divergent study designs, the inclusion of patients in different stages of their disease and small sample sizes, the inconsistent findings of motor activation studies are not surprising. Whilst, hypo- (29, 30, 40) and hyper-activation (13, 14, 16, 35, 36) of the somatosensory cortex have both been reported in response to motor tasks, the recruitment of premotor areas is a relatively consistent finding. An integrative explanation of the seemingly divergent findings is that the initial hyper-activation represents an early-stage adaptive process to execute movement (14), which gradually gives place to hypo-activation as progressive structural changes ensue (41, 42). Robust multi-timepoint longitudinal studies are required to clarify the timeline of functional changes in ALS as very few task-based longitudinal FMRI studies have been published to date (33, 43). One longitudinal study identified reduced motor activation on 3-month follow-up which was interpreted as compensatory failure due to progressive neural loss (43), while another study reported increased precentral gyrus activity 6-month after initial scanning as evidence of ongoing adaptation (33). In addition to compensatory processes in motor, premotor and supplementary motor areas (44), evidence also exist that the basal ganglia (17, 18, 45, 46), the ipsilateral motor cortex (14, 47), and the cerebellum (17, 19, 47, 48) also contribute to adaptive network reorganization.
Extra-Motor Studies
With the increasing recognition of cognitive impairment in ALS (49, 50), a series of elegant language (51), executive (52), theory-of-mind (36), and memory (43, 53, 54) task-based activation studies have also been published. In addition to the cognitive activation paradigms, visual, auditory and somatosensory stimulation studies have further characterized the spectrum of extra-motor involvement in ALS (55, 56). Other innovative non-motor activation studies in ALS include an anti-saccade study with concurrent eye tracking to investigate dorsolateral prefrontal cortex (DLPFC) function (57). Similar to the divergent findings of motor-task studies, increased activation (36, 52, 58, 59) and impaired activation (51, 60) have both been noted on cognitive tasks, which is likely to represent stages of successful and failing adaptation. More often however a pattern of coexisting hypo- and hyper- activation is reported (37, 54, 61).
Resting-State Studies
The analysis of task-free BOLD signal in the so-called resting-state (rsFMRI) benefits from fast acquisition times with a data-driven, more consistent experimental design, making them an attractive add-on to high-resolution structural protocols. With the establishment of the internationally collaborative Neuroimaging Society in ALS (NiSALS) (62) and successful multi-site initiatives (63), there is interest in FMRI sequence harmonization and potential for multicentre data pooling (12, 64). rsFMRI studies differ considerably in their analysis approaches and their methods span from independent component analysis (65–67), to graph theory (68, 69) and amplitude of low frequency fluctuation (ALFF) (70, 71). rsFMRI studies in ALS identified decreased frontotemporal (72), sensorimotor (70, 73–75), and cortical-subcortical (76) network integrity and increased default mode network (75), and cerebellar (38, 69) connectivity. Large combined structural-FMRI studies suggest that patterns of structural degeneration overlap with functionally impaired regions and that a strong positive correlation exists between functional and structural connectivity alterations (77). Longitudinal rsFMRI studies indicate declining functional connectivity in sensorimotor, thalamic, and visual networks and increasing connectivity in fronto-parietal and temporal circuits (39). Multimodal, structural-functional, multi-timepoint longitudinal studies (39) are best suited to characterize the natural history of progressive neurodegenerative changes (78). Data from presymptomatic carriers of ALS-causing gene mutations revealed increased cerebello-cerebral functional connectivity (79) and decreased salience, sensorimotor, default-mode, and thalamic networks connectivity (80). Despite the controversy around direct clinico-radiological correlations (81), some studies in ALS have reported significant associations, most often with functional measures (73, 82–84), disease duration (59, 73), and progression rates (20, 40, 85).
Practical Limitations
For a condition in which accumulation of physical disability is accompanied by ventilatory compromise with orthopnoea, supine MRI limits longitudinal assessment to those with slower rates of progression (39). The application of such a biomarker as an outcome measure in a small-scale clinical trial would then entail costly statistical compromises, since no ideal solution exists for the imputation of data points selectively lost from those patients with more aggressive disease (86).
Encephalography
Cortical processes, and the diseases that impact on them, are inadequately described without reference to dynamic neural communication (87, 88), but this necessitates temporal precision, without the dispersive effects of the haemodynamic response function that smears neural signals across several seconds (89). Surface electroencephalography (EEG) as a biomarker in ALS is appealingly practical, well tolerated and non-invasive.
Methodological Limitations
Even a high-density array of surface EEG electrodes still sacrifices spatial resolution owing to the attenuation and mutation of neural signals as they pass through several tissue layers with varying electrical conductivity (89). Magnetoencephalography (MEG) permits recording of tiny (femtoTesla) fluctuations in the magnetic field external to (and undispersed by) the scalp (90). Yet reconstruction of cortical sources remains a mathematically “ill-posed” problem—any given recorded signal could in theory be generated by multiple neural sources and the analytical choice to address this (for example “beamforming”) necessitates certain assumptions (91). MEG's improvement in spatial precision is also offset by expenses and susceptibility to artifact from ferromagnetic interference, albeit mitigated by acquisition and analysis standardization (92, 93). The resulting data is feature-rich, subsequent analysis may necessarily be restricted to a frequency-band of interest or a selected connectivity metric, these choices may in turn influence study conclusions (94) (Table 2).
Table 2.
Authors | Year | n | EEG/MEG (channels) | Protocol | Main measure | Phenotype correlations |
---|---|---|---|---|---|---|
Westphal et al. (104) | 1998 | 16 ALS 16 HC |
EEG (11c) |
Self-paced R fist closure | Reduced BP | Spasticity correlation |
Thorns et al. (105) | 2010 | 13 ALS 13 HC |
EEG (19c) |
Cued R or L index finger button press | Reduced BP | N/A |
Inuggi et al. (107) | 2011 | 32 ALS 12 HC |
EEG (29c) |
Self-paced R thumb extension | Reduced MRCPs (only in UMN+ ALS) | Ipsilateral MRCP correlation with movement speed |
Riva et al. (119) | 2012 | 16 ALS 15 HC |
EEG (29c) |
Self-paced R thumb extension | Reduced beta ERS; Unaltered beta ERD |
ERS correlation with CST damage via MRI and TMS |
Gu et al. (108) | 2013 | 4 ALS 7 HC |
EEG (15c) |
Imaginary R wrist extension | Slower MRCP rebound | N/A |
Bizovičar et al. (120) | 2014 | 21 ALS 19 HC |
EEG (30c) |
Self-paced R index finger flexion | Reduced beta ERD; Lateralised ERS |
None |
Proudfoot et al. (122) | 2017 | 11 ALS 9 PLS 12 Presymp 10 HC |
MEG (306c) | Cued R or L index finger extension | Excess beta ERD; Delayed ERS |
Altered ERS lateralisation in PLS |
Proudfoot et al. (126) | 2018 | 17 ALS 11 HC 5 Presymp |
MEG (306c) | Cued R and L hand grips | Reduced beta CMC; Reduced inter-hemispheric beta FC |
CMC unaltered in Presymp |
Mai et al. (143) | 1998 | 18 ALS 14 HC |
EEG (18c) |
Resting | Reduced central alpha power | Alpha correlation with MRC and Norris scales |
Santhosh et al. (144) | 2005 | 12 ALS 12 ALS |
EEG (8c) |
Resting | Reduced alpha power | N/A |
Jayaram et al. (145) | 2015 | 6 ALS 32 HC |
EEG (124c) | Resting | Reduced central theta power; Widespread increased high gamma power | Gamma reduced only in patient with ALSFRS = 0 |
Iyer et al. (147) | 2015 | 18 ALS 17 HC |
EEG (128c) | Resting | Increased FC especially within salience and default-mode networks | N/A |
Nasseroleslami et al. (148) | 2017 | 100 ALS 34 HC |
EEG (128c) | Resting | Increased FC especially interhemispheric theta and fronto-parietal gamma | FC correlation with structural MRI degeneration |
Fraschini et al. (150) | 2018 | 21 ALS 16 HC |
EEG (61c) |
Resting | Widespread reduced alpha FC | N/A |
Proudfoot et al. (151) | 2018 | 24 ALS 24 HC 15 Presymp 9 PLS |
MEG (306c) | Resting | Widespread increased broadband FC | Similar changes in PLS. More subtle changes in Presymp. |
Sorrentino et al. (154) | 2018 | 50 ALS 25 HC |
MEG (163c) | Resting | Broadband increased FC with disorganized topology | Advanced ALS associated with a more centralized, “vulnerable” network |
HC healthy control, FC functional connectivity, Presymp asymptomatic ALS-causing gene carriers, BP Bereitschaftspotential, MRCP movement related cortical potential, ERD event related desynchronization, ERS event related synchronization PLS primary lateral sclerosis, CMC cortico-muscular coherence UMN+ above average quantity of upper motor neuron signs, CST cortico-spinal tract, ALSFRS ALS functional rating scale (disability metric).
Evoked Potentials
Small-scale EEG studies have addressed the utility of somatosensory, visual and brainstem evoked potentials in ALS (95–100). Reflecting the inconsistency of reported results, these well-established and standardized assessments have failed to find any routine clinical application in ALS, although they may yet find a role in multimodal assessment (101, 102).
To better reflect the pathological burden in ALS studies have therefore moved toward either motor or cognitive activation paradigms, initially appraising cortical processes via evoked response potentials [ERPs, previously reviewed in (103)]. The “Bereitschaftspotential,” a classical lateralized change in cortical electrical potential, easily recordable during movement preparation, appeared robustly decreased in ALS (104, 105). More recent studies have considered the implications of abnormal movement-related cortical potentials (MRCPs) in ALS in terms of clinical and structural correlates. While a study of 21 ALS patients demonstrated higher MRCPs overall, the effect was shown to be driven by patients with a low burden of clinically detectable UMN morbidity (106). The inference that increased MRCPs reflect cortical compensatory mechanisms was born out by longitudinal study of a sub-set in whom MRCPs declined over 10 months. A comparable study of finger movement in 32 ALS patients revealed reduced MRCPs only in patients with a high UMN burden, alongside evidence of ipsilateral premotor activation to suggest a compensatory “boundary shift” (107). MRCPs are also elicited during imagined movements, but only a limited study in ALS has thus far been performed (108), mandating replication before application of these measures in control and communication devices is to be seriously considered.
Motor Paradigms
Motor events (including self-generated movement) are reflected in frequency-specific changes to continuous “background” neuronal oscillations (109). As might be expected, the neurodegeneration associated with ALS results in distinct alteration to pre-central sensorimotor rhythms. While studies are yet to be widely replicated, they show promise both in terms of relevance to daily motor tasks, and sensitivity to detect early cortical dysfunction in patients still capable of performing the task in question. The results may also contribute to the ongoing efforts to characterize a presymptomatic phase to ALS and have implications for the development of brain-computer interfaces aiming to facilitate environmental control by patients with advanced ALS (110).
Movement is accompanied by reliable and well-characterized fluctuations in neural signal power, particularly within the beta (15–30 Hz) band, with recognizable anatomical localization to motor cortex. Beta-band power is reduced (event related desynchronization, ERD) prior to and during movement execution; movement termination is followed by an equally reliable increase in power well above baseline levels (synchronization, ERS or post-movement beta-rebound) (111). Temporally corresponding to fluctuations in cortical excitability (112), ERD and ERS are adjusted to meet task requirements [including force (113), speed (114), and complexity (115)], are sensitive to pharmacological manipulation [particularly synaptic GABA levels via benzodiazepines (116) or tiagabine (117)] and may be disrupted by other disease states including Parkinson's (118).
Motor Studies (EEG)
Two independent EEG studies have demonstrated attenuation of ERS in ALS. The first involved 16 patients efficiently performing self-paced thumb extensions (119). The degree of ERS attenuation was shown to correlate with corticospinal pathological burden as measured by both mean diffusivity on structural MRI and diminished motor evoked potentials in APB in response to TMS stimulation. The second study included 21 patients performing both sniffing and right index finger flexion (120). Although the patients had detectable weakness in terms of both maximal grip strength and sniff nasal-inspiratory pressure, there were no group differences in the precise pressure produced during the task performance. Neural data from the sniff task were heavily contaminated by facial muscle artifact, but the finger flexion task resulted in reliable ERD/S. The ALS patients were observed to have diminished beta ERD, interpreted as a consequence of pyramidal cell degeneration. Both motor preparation and execution timepoints were affected, while the lateralization of beta ERS was also altered. The study failed to establish clinical correlations with these measures, nor was there any successful correlation with F-wave elicitability (an imperfect measure of corticospinal tract integrity in any case).
Motor Studies (MEG)
The neural signal acquired by MEG is far less susceptible to distortion as it passes through skull and scalp, source modeling is therefore likely to be more accurate than EEG, and an expanding range of MEG studies have specifically appraised sensorimotor rhythms (121). A MEG study involving 11 ALS patients, 9 with PLS, and 12 asymptomatic genetic mutation carriers, investigated sensorimotor rhythms during a laterally-cued motor preparation task requiring speeded index finger extension of either hand (122). Whole-brain source-space data were analyzed pre, during, and post movement, specifically focusing on beta-band frequencies. Although the task was behaviorally performed comparably by ALS patients, the neural data revealed larger beta ERD, 500 ms after cue presentation, during the period of maximal motor preparation, particularly within contra and ipsilateral gyri. Beta ERS, after movement termination, was delayed in both patient groups. The asymptomatic carriers produced excessive beta ERD during motor execution. Conceptually the results are concordant with cross-modality support for cortical hyperexcitability in ALS (123, 124).
The integrity of upper motor neurone pathways can also be non-invasively appraised using MEG. Cortico-muscular coherence (CMC), by which neural oscillations and surface electromyography correlate temporally (particularly during sustained contraction), principally reflects direct corticospinal drive to the peripheral musculature (125). A MEG study of 17 ALS patients was designed to measure CMC during a bilateral forearm grip task (126). As expected, source-space beta CMC was distinctly strongest from the contra-lateral precentral gyrus, but this frequency specific peak was markedly attenuated in the ALS group, despite adequate grip production and without any correlation to force production. The analysis also took advantage of MEG spatial precision to consider motoric cortico-cortical communication during the same task performance. Interhemispheric functional connectivity, in terms of beta band amplitude envelope correlation, was reduced in ALS patients. The inference of reduced CMC, a measure that in health indexes the quality of motor performance (127), is that beta coherence may serve as a novel UMN specific biomarker at the disposal of future therapeutic efforts (128).
Extra-Motor Studies
Taking advantage of the high temporal resolution of encephalographic data, component steps in the complex cognitive dysfunction associated with the ALS-FTD syndrome may be examined. The mismatch negativity (MMN) paradigm considers the attentional modulation of auditory perception. An early EEG study failed to show any abnormalities within ALS patients (129). However, using MEG, plus subtle experimental design adjustments in 12 participants all with bulbar symptoms, MMN response amplitudes were shown to increase relative to healthy controls (130). Given the previously demonstrated sensitivity of MMN responses to ketamine administration, the authors tentatively linked their findings to the glutamatergic excitotoxicity ALS pathogenesis theory. This rare example of “gain of function” was not consistently replicated in two later EEG studies, which interpreted delayed MMN responses as evidence of sub-clinical extra-motor dysfunction (131, 132).
Less well-replicated methodologies have also been applied to ALS patients to consider neural processes underlying working memory (133, 134), selective attention (135, 136), and executive control (137, 138). Broadly, these studies have provided further evidence in favor of sub-clinical disruption to “frontal” cognitive processes in keeping with the extended non-motor ALS phenotype (139). Parietal cortex dysfunction was also implicated in an EEG study involving the Wisconsin Card Sorting Test. While 26 ALS patients did not differ in performance of a “set-shifting” task, even patients without mild cognitive impairment failed to produce the expected enhancement of parietal ERPs during a task-switch (140). Although the attenuation of the “switch potential” failed to correlate with neuropsychological indices, the authors speculated whether such sub-clinical deficits could predict future behavioral disorder.
A study requiring cognitive task performance during data acquisition took a very different analytical approach, using 200 s of data to measure “transfer entropy” between scalp electrodes rather than the millisecond granularity of evoked potentials. The directionality of functional connectivity was appraised via EEG in 18 ALS patients, revealing only feedforward (parietal to frontal) connectivity to increase across a broad frequency band (141). As the patients engaged in a spelling task with a view to brain-computer interfacing, sensory (visual) stimuli were hypothesized to be more readily processed in compensation for the diminished proprioceptive input resulting from physical disability, but an alternative explanation in terms of failing cortical inhibition was also acknowledged (20).
Resting-State Studies (EEG)
The earliest EEG investigations of ALS reflected the emerging concept of cognitive dysfunction within the ALS clinical spectrum, with slowed cortical rhythms noted in non-demented patients (142). A more systematic study of 18 ALS patients conversely revealed sparse differences to healthy controls (143). Only at central electrodes, and only within the alpha band (8–13 Hz), was the power of neural oscillations reduced in ALS. The reduction was interpreted to reflect selective neuronal loss within the sensorimotor cortices. A comparable result was described in a subsequent smaller study (144), and increases in the gamma band (30–90 Hz) power beyond central regions was also reported (145).
Further ALS electrophysiology studies have reflected a growing interest in the so-called “dynome” (146), the extent to which the organization of cortical function is reflected in particular patterns of active connectivity. High-density (128 channel) surface EEG was used to calculate connectivity between both scalp points and projected source nodes in an initial study of 18 patients (147). Fronto-central areas were shown to have increased connectivity, and this was explored across a broad range of measures. A subsequent study expanded this work to 100 patients, including some longitudinal analyses (148), and confirmed EEG-derived connectivity changes in ALS to be more striking than limited group differences in the scalp-recorded power spectrum. This more parsimonious analysis appraised only sensor-space, deriving coherence estimates within 8 consecutive frequency bands. Widespread increases in connectivity were again demonstrated relative to healthy controls, particularly theta band interhemispheric sensorimotor connectivity and gamma band fronto-parietal connectivity. As 59 of the ALS patients had undergone contemporaneous structural MRI, mathematically derived structural “degeneration modes” (accounting for the large-scale gray and white matter changes typical in ALS) were shown to correlate with EEG change, conceptually aligned with the concept of progressive network decline overlying structural disintegration.
Network structures can also be summarized using graph theory metrics, this was explored in sensor-space in 21 patients, demonstrating a more “de-centralised” organization (149). The connectivity metric chosen in this study was phase-based, thus insensitive to any group differences in spectral power, and furthermore was significantly correlated with disability between individuals. This group later re-analyzed the same data reconstructed into source-space (150) and filtered into 3 classical frequency bands to show spatially distributed decreases in connectivity, albeit restricted to the alpha band spectrum.
Resting-State Studies (MEG)
A resting-state MEG study explored functional connectivity in 24 ALS patients using source-space data acquired after co-registration with structural MRI (151). Ten minutes of continuous data was parcellated into 39 regions of interest and the broad-band (3–40 Hz) signal used to calculate “edge” strength between these 39 “nodes,” In keeping with many FMRI studies, functional connectivity was broadly increased in ALS patients relative to age-matched healthy controls, particularly affecting communication links to the posterior cingulate cortex. This finding was aligned with the hypothesis of loss of cortical inhibitory neuronal influences underlying cortical excitability in ALS (5). Comparable posterior non-motor connectivity changes were described using FMRI (152). Nevertheless, the diversity of reported results and interpretations serves to highlight a need for replication and standardization between centers and where possible across modalities (153). A further study of 50 patients, using a different (phase-based) connectivity measure, also described widespread connectivity increases in ALS (154). The increases were not restricted to specific frequency bands and the extracted graph theory metrics suggest global network hyper-centralization to accompany disease progression.
Future Directions
MEG is providing broader insight into cognitive mechanisms underpinning higher cortical function in health (155), and comparable results may eventually prove achievable using surface EEG (156). The next generation of wearable sensors may yet dramatically expand MEG's application (157). The spinal cord is a core but functionally understudied aspect of the motor system disintegration that characterizes ALS. Spinal FMRI is in its infancy (158), but a number of promising studies have already been published in animal models (159, 160), healthy populations (161, 162) and other clinical cohorts (163, 164). The goal of non-invasively studying the integrated activity of upper and lower motor neurone pools looks more feasible with the success in studies involving the dorsal pathways (165). Cerebral FMRI parameters are likely to take an increasing role in emerging machine-learning and classification studies both in diagnostic and prognostic applications (67, 76). Future studies need robust longitudinal design and to capitalize on the growing infrastructure for multicentre studies. This will permit the testing of pathogenic hypotheses within larger cohorts of clinically more homogeneous ALS patients, and define the earliest markers of pathology in presymptomatic individuals essential for the assessment of future neurotherapeutic interventions.
Author Contributions
All authors listed have made a substantial, direct and intellectual contribution to the work, and approved it for publication.
Conflict of Interest Statement
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
Footnotes
Funding. PB is supported by the Health Research Board (HRB–Ireland; HRB EIA-2017-019), the Irish Institute of Clinical Neuroscience IICN–Novartis Ireland Research Grant, the Iris O'Brien Foundation, and the Research Motor Neuron (RMN-Ireland) Foundation.
References
- 1.Seeley WW, Crawford RK, Zhou J, Miller BL, Greicius MD. Neurodegenerative diseases target large-scale human brain networks. Neuron (2009) 62:42–52. 10.1016/j.neuron.2009.03.024 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 2.Turner MR, Swash M. The expanding syndrome of amyotrophic lateral sclerosis: a clinical and molecular odyssey. J Neurol Neurosurg Psychiatry (2015) 86:667–73. 10.1136/jnnp-2014-308946 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 3.Talbot K, Feneberg E, Scaber J, Thompson AG, Turner MR. Amyotrophic lateral sclerosis: the complex path to precision medicine. J Neurol. (2018) 265:2454–62. 10.1007/s00415-018-8983-8 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 4.Turner MR, Verstraete E. What does imaging reveal about the pathology of amyotrophic lateral sclerosis? Curr Neurol Neurosci Rep. (2015) 15:45. 10.1007/s11910-015-0569-6 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 5.Kew JJM, Leigh PN, Playford ED, Passingham RE, Goldstein LH, Frackowiak RSJ, et al. Cortical function in amyotrophic lateral sclerosis: a positron emission tomography study. Brain (1993) 116:655–80. 10.1093/brain/116.3.655 [DOI] [PubMed] [Google Scholar]
- 6.Turner MR, Kiernan MC. Does interneuronal dysfunction contribute to neurodegeneration in amyotrophic lateral sclerosis? Amyotroph Lateral Scler. (2012) 13:245–50. 10.3109/17482968.2011.636050 [DOI] [PubMed] [Google Scholar]
- 7.Yokota T, Yoshino A, Inaba A, Saito Y. Double cortical stimulation in amyotrophic lateral sclerosis. J Neurol Neurosurg Psychiatry (1996) 61:596–600. 10.1136/jnnp.61.6.596 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 8.Menon P, Geevasinga N, Yiannikas C, Howells J, Kiernan MC, Vucic S. Sensitivity and specificity of threshold tracking transcranial magnetic stimulation for diagnosis of amyotrophic lateral sclerosis: a prospective study. Lancet Neurol. (2015) 14:478–84. 10.1016/S1474-4422(15)00014-9 [DOI] [PubMed] [Google Scholar]
- 9.Benatar M, Wuu J, Ravits J. Opportunity and innovation in studying pre-symptomatic amyotrophic lateral sclerosis. Muscle Nerve (2013) 47:629–31. 10.1002/mus.23782 [DOI] [PubMed] [Google Scholar]
- 10.Vucic S, Nicholson GA, Kiernan MC. Cortical hyperexcitability may precede the onset of familial amyotrophic lateral sclerosis. Brain (2008) 131:1540–50. 10.1093/brain/awn071 [DOI] [PubMed] [Google Scholar]
- 11.Bede P. Deciphering neurodegeneration. Neurology (2017) 89:1758–9. 10.1212/WNL.0000000000004582 [DOI] [PubMed] [Google Scholar]
- 12.Bede P, Querin G, Pradat P-F. The changing landscape of motor neuron disease imaging: the transition from descriptive studies to precision clinical tools. Curr Opin Neurol. (2018) 31:431–8. 10.1097/WCO.0000000000000569 [DOI] [PubMed] [Google Scholar]
- 13.Kollewe K, Münte TF, Samii A, Dengler R, Petri S, Mohammadi B. Patterns of cortical activity differ in ALS patients with limb and/or bulbar involvement depending on motor tasks. J Neurol. (2011) 258:804–10. 10.1007/s00415-010-5842-7 [DOI] [PubMed] [Google Scholar]
- 14.Mohammadi B, Kollewe K, Samii A, Dengler R, Münte TF. Functional neuroimaging at different disease stages reveals distinct phases of neuroplastic changes in amyotrophic lateral sclerosis. Hum Brain Mapp. (2011) 32:750–8. 10.1002/hbm.21064 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 15.Poujois A, Schneider FC, Faillenot I, Camdessanché J-P, Vandenberghe N, Thomas-Antérion C, et al. Brain plasticity in the motor network is correlated with disease progression in amyotrophic lateral sclerosis. Hum Brain Mapp. (2013) 34:2391–401. 10.1002/hbm.22070 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 16.Stanton BR, Williams VC, Leigh PN, Williams SCR, Blain CRV, Jarosz JM, et al. Altered cortical activation during a motor task in ALS. Evidence for involvement of central pathways. J Neurol. (2007) 254:1260–7. 10.1007/s00415-006-0513-4 [DOI] [PubMed] [Google Scholar]
- 17.Konrad C, Jansen A, Henningsen H, Sommer J, Turski PA, Brooks BR, et al. Subcortical reorganization in amyotrophic lateral sclerosis. Exp Brain Res. (2006) 172:361–9. 10.1007/s00221-006-0352-7 [DOI] [PubMed] [Google Scholar]
- 18.Tessitore A, Esposito F, Monsurrò MR, Graziano S, Panza D, Russo A, et al. Subcortical motor plasticity in patients with sporadic ALS: An fMRI study. Brain Res Bull. (2006) 69:489–94. 10.1016/j.brainresbull.2006.01.013 [DOI] [PubMed] [Google Scholar]
- 19.Prell T, Grosskreutz J. The involvement of the cerebellum in amyotrophic lateral sclerosis. Amyotroph Lateral Scler Frontotemporal Degener. (2013) 14:507–15. 10.3109/21678421.2013.812661 [DOI] [PubMed] [Google Scholar]
- 20.Douaud G, Filippini N, Knight S, Talbot K, Turner MR. Integration of structural and functional magnetic resonance imaging in amyotrophic lateral sclerosis. Brain (2011) 134:3470–9. 10.1093/brain/awr279 [DOI] [PubMed] [Google Scholar]
- 21.Bede P, Hardiman O. Lessons of ALS imaging: pitfalls and future directions - A critical review. NeuroImage Clin. (2014) 4:436–43. 10.1016/j.nicl.2014.02.011 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 22.Buckner RL, Bandettini PA, O'Craven KM, Savoy RL, Petersen SE, Raichle ME, et al. Detection of cortical activation during averaged single trials of a cognitive task using functional magnetic resonance imaging. Proc Natl Acad Sci USA. (1996) 93:14878–83. 10.1073/pnas.93.25.14878 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 23.Ogawa S, Lee T-M, Stepnoski R, Chen W, Zhu X-H, Ugurbil K. An approach to probe some neural systems interaction by functional MRI at neural time scale down to milliseconds. Proc Natl Acad Sci USA. (2000) 97:11026–31. 10.1073/pnas.97.20.11026 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 24.Yang QX, Dardzinski BJ, Li S, Eslinger PJ, Smith MB. Multi-gradient echo with susceptibility inhomogeneity compensation (MGESIC): demonstration of fMRI in the olfactory cortex at 3.0 T. Magn Reson Med. (1997) 37:331–5. 10.1002/mrm.1910370304 [DOI] [PubMed] [Google Scholar]
- 25.Weiger M, Pruessmann KP, Österbauer R, Börnert P, Boesiger P, Jezzard P. Sensitivity-encoded single-shot spiral imaging for reduced susceptibility artifacts in BOLD fMRI. Magn Reson Med. (2002) 48:860–6. 10.1002/mrm.10286 [DOI] [PubMed] [Google Scholar]
- 26.Stenger VA, Boada FE, Noll DC. Three-dimensional tailored RF pulses for the reduction of susceptibility artifacts in T2/(*)-weighted functional MRI. Magn Reson Med. (2000) 44:525–31. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 27.Gaab N, Gabrieli JDE, Glover GH. Resting in peace or noise: Scanner background noise suppresses default-mode network. Hum Brain Mapp. (2008) 29:858–67. 10.1002/hbm.20578 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 28.Gaab N, Gabrieli JDE, Glover GH. Assessing the influence of scanner background noise on auditory processing. I. An fMRI study comparing three experimental designs with varying degrees of scanner noise. Hum Brain Mapp. (2007) 28:703–20. 10.1002/hbm.20298 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 29.Li S, Chen Q, Yu B, Xue K, Luo C, Xu Y, et al. Structural and functional changes mapped in the brains of amyotrophic lateral sclerosis patients with/without dysphagia: A pilot study. Amyotroph Lateral Scler. (2009) 10:280–7. 10.3109/17482960902893342 [DOI] [PubMed] [Google Scholar]
- 30.Mohammadi B, Kollewe K, Samii A, Krampfl K, Dengler R, Münte TF. Decreased brain activation to tongue movements in amyotrophic lateral sclerosis with bulbar involvement but not Kennedy syndrome. J Neurol. (2009) 256:1263–9. 10.1007/s00415-009-5112-8 [DOI] [PubMed] [Google Scholar]
- 31.Szameitat AJ, Shen S, Sterr A. Motor imagery of complex everyday movements. An fMRI study. Neuroimage (2007) 34:702–13. 10.1016/j.neuroimage.2006.09.033 [DOI] [PubMed] [Google Scholar]
- 32.Marchesotti S, Bassolino M, Serino A, Bleuler H, Blanke O. Quantifying the role of motor imagery in brain-machine interfaces. Sci Rep. (2016) 6:24076. 10.1038/srep24076 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 33.Lulé D, Diekmann V, Kassubek J, Kurt A, Birbaumer N, Ludolph AC, et al. Cortical plasticity in amyotrophic lateral sclerosis: motor imagery and function. Neurorehabil Neural Repair. (2007) 21:518–26. 10.1177/1545968307300698 [DOI] [PubMed] [Google Scholar]
- 34.Stanton BR, Williams VC, Leigh PN, Williams SCR, Blain CRV, Giampietro VP, et al. Cortical activation during motor imagery is reduced in Amyotrophic Lateral Sclerosis. Brain Res. (2007) 1172:145–51. 10.1016/j.brainres.2007.07.044 [DOI] [PubMed] [Google Scholar]
- 35.Li H, Chen Y, Li Y, Yin B, Tang W, Yu X, et al. Altered cortical activation during action observation in amyotrophic lateral sclerosis patients: a parametric functional MRI study. Eur Radiol. (2015) 25:2584–92. 10.1007/s00330-015-3671-x [DOI] [PubMed] [Google Scholar]
- 36.Jelsone-Swain L, Persad C, Burkard D, Welsh RC. Action processing and mirror neuron function in patients with amyotrophic lateral sclerosis: an fMRI study. PLoS ONE (2015) 10:e0119862. 10.1371/journal.pone.0119862 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 37.Lulé D, Diekmann V, Anders S, Kassubek J, Kübler A, Ludolph AC, et al. Brain responses to emotional stimuli in patients with amyotrophic lateral sclerosis (ALS). J Neurol. (2007) 254:519–27. 10.1007/s00415-006-0409-3 [DOI] [PubMed] [Google Scholar]
- 38.Meoded A, Morrissette AE, Katipally R, Schanz O, Gotts SJ, Floeter MK. Cerebro-cerebellar connectivity is increased in primary lateral sclerosis. NeuroImage Clin. (2015) 7:288–96. 10.1016/j.nicl.2014.12.009 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 39.Menke RAL, Proudfoot M, Talbot K, Turner MR. The two-year progression of structural and functional cerebral MRI in amyotrophic lateral sclerosis. NeuroImage Clin. (2018) 17:953–61. 10.1016/j.nicl.2017.12.025 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 40.Cosottini M, Pesaresi I, Piazza S, Diciotti S, Cecchi P, Fabbri S, et al. Structural and functional evaluation of cortical motor areas in Amyotrophic Lateral Sclerosis. Exp Neurol. (2012) 234:169–80. 10.1016/j.expneurol.2011.12.024 [DOI] [PubMed] [Google Scholar]
- 41.Bede P, Hardiman O. Longitudinal structural changes in ALS: a three time-point imaging study of white and gray matter degeneration. Amyotroph Lateral Scler Front Degener. (2018) 19:232–41. 10.1080/21678421.2017.1407795 [DOI] [PubMed] [Google Scholar]
- 42.Agosta F, Valsasina P, Absinta M, Riva N, Sala S, Prelle A, et al. Sensorimotor functional connectivity changes in amyotrophic lateral sclerosis. Cereb Cortex (2011) 21:2291–8. 10.1093/cercor/bhr002 [DOI] [PubMed] [Google Scholar]
- 43.Stoppel CM, Vielhaber S, Eckart C, Machts J, Kaufmann J, Heinze H-J, et al. Structural and functional hallmarks of amyotrophic lateral sclerosis progression in motor- and memory-related brain regions. NeuroImage Clin. (2014) 5:277–90. 10.1016/j.nicl.2014.07.007 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 44.Konrad C, Henningsen H, Bremer J, Mock B, Deppe M, Buchinger C, et al. Pattern of cortical reorganization in amyotrophic lateral sclerosis: a functional magnetic resonance imaging study. Exp brain Res. (2002) 143:51–6. 10.1007/s00221-001-0981-9 [DOI] [PubMed] [Google Scholar]
- 45.Bede P, Omer T, Finegan E, Chipika RH, Iyer PM, Doherty MA, et al. Connectivity-based characterisation of subcortical grey matter pathology in frontotemporal dementia and ALS: a multimodal neuroimaging study. Brain Imaging Behav. (2018) 12:1696–1707. 10.1007/s11682-018-9837-9 [DOI] [PubMed] [Google Scholar]
- 46.Feron M, Couillandre A, Mseddi E, Termoz N, Abidi M, Bardinet E, et al. Extrapyramidal deficits in ALS: a combined biomechanical and neuroimaging study. J Neurol. (2018) 265:1–12. 10.1007/s00415-018-8964-y [DOI] [PubMed] [Google Scholar]
- 47.Schoenfeld MA, Tempelmann C, Gaul C, Kühnel GR, Düzel E, Hopf J-M, et al. Functional motor compensation in amyotrophic lateral sclerosis. J Neurol. (2005) 252:944–52. 10.1007/s00415-005-0787-y [DOI] [PubMed] [Google Scholar]
- 48.Han J, Ma L. Functional magnetic resonance imaging study of the brain in patients with amyotrophic lateral sclerosis. Chinese Med Sci J. (2006) 21:228–33. [PubMed] [Google Scholar]
- 49.Elamin M, Bede P, Byrne S, Jordan N, Gallagher L, Wynne B, et al. Cognitive changes predict functional decline in ALS: a population-based longitudinal study. Neurology (2013) 80:1590–7. 10.1212/WNL.0b013e31828f18ac [DOI] [PubMed] [Google Scholar]
- 50.Elamin M, Phukan J, Bede P, Jordan N, Byrne S, Pender N, et al. Executive dysfunction is a negative prognostic indicator in patients with ALS without dementia. Neurology (2011) 76:1263–9. 10.1212/WNL.0b013e318214359f [DOI] [PubMed] [Google Scholar]
- 51.Abrahams S, Goldstein LH, Simmons A, Brammer M, Williams SCR, Giampietro V, et al. Word retrieval in amyotrophic lateral sclerosis: a functional magnetic resonance imaging study. Brain (2004) 127:1507–17. 10.1093/brain/awh170 [DOI] [PubMed] [Google Scholar]
- 52.Goldstein LH, Newsom-Davis IC, Bryant V, Brammer M, Leigh PN, Simmons A. Altered patterns of cortical activation in ALS patients during attention and cognitive response inhibition tasks. J Neurol. (2011) 258:2186–98. 10.1007/s00415-011-6088-8 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 53.Palmieri A, Naccarato M, Abrahams S, Bonato M, D'Ascenzo C, Balestreri S, et al. Right hemisphere dysfunction and emotional processing in ALS: An fMRI study. J Neurol. (2010) 257:1970–8. 10.1007/s00415-010-5640-2 [DOI] [PubMed] [Google Scholar]
- 54.Vellage AK, Veit M, Kobeleva X, Petri S, Vielhaber S, Müller NG. Working memory network changes in ALS: An fMRI study. Front Neurosci. (2016) 10:158. 10.3389/fnins.2016.00158 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 55.Lulé D, Diekmann V, Müller HP, Kassubek J, Ludolph AC, Birbaumer N. Neuroimaging of multimodal sensory stimulation in amyotrophic lateral sclerosis. J Neurol Neurosurg Psychiatry (2010) 81:899–906. 10.1136/jnnp.2009.192260 [DOI] [PubMed] [Google Scholar]
- 56.Bede P, Iyer PM, Schuster C, Elamin M, Mclaughlin RL, Kenna K, et al. The selective anatomical vulnerability of ALS: ‘disease-defining’ and ‘disease-defying’ brain regions. Amyotroph Lateral Scler Front Degener. (2016) 8421:1–10. 10.3109/21678421.2016.1173702 [DOI] [PubMed] [Google Scholar]
- 57.Witiuk K, Fernandez-Ruiz J, McKee R, Alahyane N, Coe BC, Melanson M, et al. Cognitive deterioration and functional compensation in ALS measured with fMRI using an inhibitory task. J Neurosci. (2014) 34:14260–71. 10.1523/JNEUROSCI.1111-14.2014 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 58.Keller J, Böhm S, Aho-Özhan HEA, Loose M, Gorges M, Kassubek J, et al. Functional reorganization during cognitive function tasks in patients with amyotrophic lateral sclerosis. Brain Imaging Behav. (2018) 12:771–84. 10.1007/s11682-017-9738-3 [DOI] [PubMed] [Google Scholar]
- 59.Passamonti L, Fera F, Tessitore A, Russo A, Cerasa A, Gioia CM, et al. Dysfunctions within limbic-motor networks in amyotrophic lateral sclerosis. Neurobiol Aging (2013) 34:2499–509. 10.1016/j.neurobiolaging.2013.05.016 [DOI] [PubMed] [Google Scholar]
- 60.Aho-Özhan HEA, Keller J, Heimrath J, Uttner I, Kassubek J, Birbaumer N, et al. Perception of emotional facial expressions in amyotrophic lateral sclerosis (ALS) at behavioural and brain metabolic level. PLoS ONE (2016) 11:e0164655. 10.1371/journal.pone.0164655 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 61.Mohammadi B, Kollewe K, Cole DM, Fellbrich A, Heldmann M, Samii A, et al. Amyotrophic lateral sclerosis affects cortical and subcortical activity underlying motor inhibition and action monitoring. Hum Brain Mapp. (2015) 36:2878–89. 10.1002/hbm.22814 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 62.Turner MR, Grosskreutz J, Kassubek J, Abrahams S, Agosta F, Benatar M, et al. Towards a neuroimaging biomarker for amyotrophic lateral sclerosis. Lancet Neurol. (2011) 10:400–3. 10.1016/S1474-4422(11)70049-7 [DOI] [PubMed] [Google Scholar]
- 63.Müller H-P, Turner MR, Grosskreutz J, Abrahams S, Bede P, Govind V, et al. A large-scale multicentre cerebral diffusion tensor imaging study in amyotrophic lateral sclerosis. J Neurol Neurosurg Psychiatry (2016) 87:570–9. 10.1136/jnnp-2015-311952 [DOI] [PubMed] [Google Scholar]
- 64.Dansereau C, Benhajali Y, Risterucci C, Pich EM, Orban P, Arnold D, et al. Statistical power and prediction accuracy in multisite resting-state fMRI connectivity. Neuroimage (2017) 149:220–32. 10.1016/J.NEUROIMAGE.2017.01.072 [DOI] [PubMed] [Google Scholar]
- 65.Agosta F, Canu E, Valsasina P, Riva N, Prelle A, Comi G, et al. Divergent brain network connectivity in amyotrophic lateral sclerosis. Neurobiol Aging (2013) 34:419–27. 10.1016/j.neurobiolaging.2012.04.015 [DOI] [PubMed] [Google Scholar]
- 66.Mohammadi B, Kollewe K, Samii A, Krampfl K, Dengler R, Münte TF. Changes of resting state brain networks in amyotrophic lateral sclerosis. Exp Neurol. (2009) 217:147–53. 10.1016/j.expneurol.2009.01.025 [DOI] [PubMed] [Google Scholar]
- 67.Welsh RC, Jelsone-Swain LM, Foerster BR. The utility of independent component analysis and machine learning in the identification of the amyotrophic lateral sclerosis diseased brain. Front Hum Neurosci. (2013) 7:251. 10.3389/fnhum.2013.00251 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 68.Li W, Zhang J, Zhou C, Hou W, Hu J, Feng H, et al. Abnormal functional connectivity density in amyotrophic lateral sclerosis. Front Aging Neurosci. (2018) 10:215. 10.3389/fnagi.2018.00215 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 69.Zhou C, Hu X, Hu J, Liang M, Yin X, Chen L, et al. Altered brain network in amyotrophic lateral sclerosis: a resting graph theory-based network study at voxel-wise level. Front Neurosci. (2016) 10:204. 10.3389/fnins.2016.00204 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 70.Sako W, Abe T, Izumi Y, Yamazaki H, Matsui N, Harada M, et al. Spontaneous brain activity in the sensorimotor cortex in amyotrophic lateral sclerosis can be negatively regulated by corticospinal fiber integrity. Neurol Sci. (2017) 38:755–60. 10.1007/s10072-017-2831-y [DOI] [PubMed] [Google Scholar]
- 71.Luo C, Chen Q, Huang R, Chen X, Chen K, Huang X, et al. Patterns of spontaneous brain activity in amyotrophic lateral sclerosis: a resting-state fMRI study. PLoS ONE (2012) 7:e45470. 10.1371/journal.pone.0045470 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 72.Trojsi F, Di Nardo F, Santangelo G, Siciliano M, Femiano C, Passaniti C, et al. Resting state fMRI correlates of theory of mind impairment in amyotrophic lateral sclerosis. Cortex (2017) 97:1–16. 10.1016/j.cortex.2017.09.016 [DOI] [PubMed] [Google Scholar]
- 73.Zhou F, Xu R, Dowd E, Zang Y, Gong H, Wang Z. Alterations in regional functional coherence within the sensory-motor network in amyotrophic lateral sclerosis. Neurosci Lett. (2014) 558:192–6. 10.1016/j.neulet.2013.11.022 [DOI] [PubMed] [Google Scholar]
- 74.Zhang J, Ji B, Hu J, Zhou C, Li L, Li Z, et al. Aberrant interhemispheric homotopic functional and structural connectivity in amyotrophic lateral sclerosis. J Neurol Neurosurg Psychiatry (2017) 88:369.1–70. 10.1136/jnnp-2016-314567 [DOI] [PubMed] [Google Scholar]
- 75.Chenji S, Jha S, Lee D, Brown M, Seres P, Mah D, et al. Investigating default mode and sensorimotor network connectivity in amyotrophic lateral sclerosis. PLoS ONE (2016) 11:e0157443. 10.1371/journal.pone.0157443 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 76.Fekete T, Zach N, Mujica-Parodi LR, Turner MR. Multiple kernel learning captures a systems-level functional connectivity biomarker signature in amyotrophic lateral sclerosis. PLoS ONE (2013) 8:e85190. 10.1371/journal.pone.0085190 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 77.Schmidt R, Verstraete E, de Reus MA, Veldink JH, van den Berg LH, van den Heuvel MP. Correlation between structural and functional connectivity impairment in amyotrophic lateral sclerosis. Hum Brain Mapp. (2014) 35:4386–95. 10.1002/hbm.22481 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 78.Schuster C, Elamin M, Hardiman O, Bede P. Presymptomatic and longitudinal neuroimaging in neurodegeneration–from snapshots to motion picture: a systematic review. J Neurol Neurosurg Psychiatry (2015) 86:1089–96. 10.1136/jnnp-2014-309888 [DOI] [PubMed] [Google Scholar]
- 79.Menke RAL, Proudfoot M, Wuu J, Andersen PM, Talbot K, Benatar M, et al. Increased functional connectivity common to symptomatic amyotrophic lateral sclerosis and those at genetic risk. J Neurol Neurosurg Psychiatry (2016) 87:580–8. 10.1136/jnnp-2015-311945 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 80.Lee SE, Sias AC, Mandelli ML, Brown JA, Brown AB, Khazenzon AM, et al. Network degeneration and dysfunction in presymptomatic C9ORF72 expansion carriers. NeuroImage Clin. (2017) 14:286–97. 10.1016/j.nicl.2016.12.006 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 81.Verstraete E, Turner MR, Grosskreutz J, Filippi M, Benatar M. Mind the gap: The mismatch between clinical and imaging metrics in ALS. Amyotroph Lateral Scler Front Degener. (2015) 16:524–9. 10.3109/21678421.2015.1051989 [DOI] [PubMed] [Google Scholar]
- 82.Zhou F, Gong H, Li F, Zhuang Y, Zang Y, Xu R, et al. Altered motor network functional connectivity in amyotrophic lateral sclerosis: a resting-state functional magnetic resonance imaging study. Neuroreport (2013) 24:657–62. 10.1097/WNR.0b013e328363148c [DOI] [PubMed] [Google Scholar]
- 83.Geevasinga N, Korgaonkar MS, Menon P, Van den Bos M, Gomes L, Foster S, et al. Brain functional connectome abnormalities in amyotrophic lateral sclerosis are associated with disability and cortical hyperexcitability. Eur J Neurol. (2017) 24:1507–17. 10.1111/ene.13461 [DOI] [PubMed] [Google Scholar]
- 84.Jelsone-Swain LM, Fling BW, Seidler RD, Hovatter R, Gruis K, Welsh RC. Reduced interhemispheric functional connectivity in the motor cortex during rest in limb-onset amyotrophic lateral sclerosis. Front Syst Neurosci. (2010) 4:158. 10.3389/fnsys.2010.00158 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 85.Verstraete E, van den Heuvel MP, Veldink JH, Blanken N, Mandl RC, Hulshoff Pol HE, et al. Motor network degeneration in amyotrophic lateral sclerosis: a structural and functional connectivity study. PLoS ONE (2010) 5:e13664. 10.1371/journal.pone.0013664 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 86.Proudfoot M, Jones A, Talbot K, Al-Chalabi A, Turner MR. The ALSFRS as an outcome measure in therapeutic trials and its relationship to symptom onset. Amyotroph Lateral Scler Frontotemporal Degener. (2016) 17:414–25. 10.3109/21678421.2016.1140786 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 87.Fries P. Rhythms for cognition: communication through coherence. Neuron (2015) 88:220–35. 10.1016/j.neuron.2015.09.034 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 88.Voytek B, Knight RT. Dynamic network communication as a unifying neural basis for cognition, development, aging, and disease. Biol Psychiatry (2015) 77:1089–97. 10.1016/j.biopsych.2015.04.016 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 89.Lopes da Silva F. EEG and MEG: relevance to neuroscience. Neuron (2013) 80:1112–28. 10.1016/j.neuron.2013.10.017 [DOI] [PubMed] [Google Scholar]
- 90.Proudfoot M, Woolrich MW, Nobre AC, Turner MR. Magnetoencephalography. Pract Neurol. (2014) 14:336–43. 10.1136/practneurol-2013-000768 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 91.Hall EL, Robson SE, Morris PG, Brookes MJ. The relationship between MEG and fMRI. Neuroimage (2014) 102:80–91. 10.1016/j.neuroimage.2013.11.005 [DOI] [PubMed] [Google Scholar]
- 92.Gross J, Baillet S, Barnes GR, Henson RN, Hillebrand A, Jensen O, et al. Good practice for conducting and reporting MEG research. Neuroimage (2013) 65:349–63. 10.1016/j.neuroimage.2012.10.001 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 93.Hari R, Baillet S, Barnes G, Burgess R, Forss N, Gross J, et al. IFCN-endorsed practical guidelines for clinical magnetoencephalography (MEG). Clin Neurophysiol. (2018) 129:1720–47. 10.1016/j.clinph.2018.03.042 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 94.van Diessen E, Numan T, van Dellen E, van der Kooi AW, Boersma M, Hofman D, et al. Opportunities and methodological challenges in EEG and MEG resting state functional brain network research. Clin Neurophysiol. (2015) 126:1468–81. 10.1016/j.clinph.2014.11.018 [DOI] [PubMed] [Google Scholar]
- 95.Bosch EP, Yamada T, Kimura J. Somatosensory evoked potentials in motor neuron disease. Muscle Nerve (1985) 8:556–62. 10.1002/mus.880080703 [DOI] [PubMed] [Google Scholar]
- 96.Ghezzi A, Mazzalovo E, Locatelli C, Zibetti A, Zaffaroni M, Montanini R. Multimodality evoked potentials in amyotrophic lateral sclerosis. Acta Neurol Scand. (1989) 79:353–6. 10.1111/j.1600-0404.1989.tb03799.x [DOI] [PubMed] [Google Scholar]
- 97.Radtke RA, Erwin A, Erwin CW. Abnormal sensory evoked potentials in amyotrophic lateral sclerosis. Neurology (1986) 36:796–801. 10.1212/WNL.36.6.796 [DOI] [PubMed] [Google Scholar]
- 98.Cosi V, Poloni M, Mazzini L, Callieco R. Somatosensory evoked potentials in amyotrophic lateral sclerosis. J Neurol Neurosurg Psychiatry (1984) 47:857–61. 10.1136/jnnp.47.8.857 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 99.Subramaniam JS, Yiannikas C. Multimodality evoked potentials in motor neuron disease. Arch Neurol. (1990) 47:989–94. 10.1001/archneur.1990.00530090063014 [DOI] [PubMed] [Google Scholar]
- 100.Soares JJF, Lawoko S, Nolan P. The nature, extent and determinants of violence against psychiatric personnel. Work Stress (2000) 14:105–20. 10.1080/026783700750051630 [DOI] [Google Scholar]
- 101.Iglesias C, Sangari S, El Mendili M-M, Benali H, Marchand-Pauvert V, Pradat P-F. Electrophysiological and spinal imaging evidences for sensory dysfunction in amyotrophic lateral sclerosis. BMJ Open (2015) 5:e007659. 10.1136/bmjopen-2015-007659 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 102.Sangari S, Iglesias C, El Mendili MM, Benali H, Pradat PF, Marchand-Pauvert V. Impairment of sensory-motor integration at spinal level in amyotrophic lateral sclerosis. Clin Neurophysiol. (2016) 127:1968–77. 10.1016/j.clinph.2016.01.014 [DOI] [PubMed] [Google Scholar]
- 103.Raggi A, Iannaccone S, Cappa SF. Event-related brain potentials in amyotrophic lateral sclerosis: a review of the international literature. Amyotroph Lateral Scler. (2010) 11:16–26. 10.3109/17482960902912399 [DOI] [PubMed] [Google Scholar]
- 104.Westphal KP, Heinemann HA, Grözinger B, Kotchoubey BJ, Diekmann V, Becker W, et al. Bereitschaftspotential in amyotrophic lateral sclerosis (ALS): lower amplitudes in patients with hyperreflexia (spasticity). Acta Neurol Scand. (1998) 98:15–21. 10.1111/j.1600-0404.1998.tb07372.x [DOI] [PubMed] [Google Scholar]
- 105.Thorns J, Wieringa BM, Mohammadi B, Hammer A, Dengler R, Münte TF. Movement initiation and inhibition are impaired in amyotrophic lateral sclerosis. Exp Neurol. (2010) 224:389–94. 10.1016/j.expneurol.2010.04.014 [DOI] [PubMed] [Google Scholar]
- 106.Bizovičar N, Koritnik B, Zidar I, Dreo J, Zidar J. Movement-related cortical potentials in ALS increase at lower and decrease at higher upper motor neuron burden scores. Amyotroph Lateral Scler Frontotemporal Degener. (2013) 14:380–9. 10.3109/21678421.2012.760604 [DOI] [PubMed] [Google Scholar]
- 107.Inuggi A, Riva N, González-Rosa JJ, Amadio S, Amato N, Fazio R, et al. Compensatory movement-related recruitment in amyotrophic lateral sclerosis patients with dominant upper motor neuron signs: an EEG source analysis study. Brain Res. (2011) 1425:37–46. 10.1016/j.brainres.2011.09.007 [DOI] [PubMed] [Google Scholar]
- 108.Gu Y, Farina D, Murguialday AR, Dremstrup K, Birbaumer N. Comparison of movement related cortical potential in healthy people and amyotrophic lateral sclerosis patients. Front Neurosci. (2013) 7:65. 10.3389/fnins.2013.00065 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 109.Pfurtscheller G, Lopes Da Silva FH. Event-related EEG/MEG synchronization and desynchronization: basic principles. Clin Neurophysiol. (1999) 110:1842–57. 10.1016/S1388-2457(99)00141-8 [DOI] [PubMed] [Google Scholar]
- 110.Vansteensel MJ, Pels EGM, Bleichner MG, Branco MP, Denison T, Freudenburg ZV, et al. Fully implanted brain–computer interface in a locked-in patient with ALS. N Engl J Med. (2016) 375:2060–6. 10.1056/NEJMoa1608085 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 111.Kilavik BE, Zaepffel M, Brovelli A, MacKay WA, Riehle A. The ups and downs of β oscillations in sensorimotor cortex. Exp Neurol. (2013) 245:15–26. 10.1016/j.expneurol.2012.09.014 [DOI] [PubMed] [Google Scholar]
- 112.Chen R, Yaseen Z, Cohen LG, Hallett M. Time course of corticospinal excitability in reaction time and self-paced movements. Ann Neurol. (1998) 44:317–25. 10.1002/ana.410440306 [DOI] [PubMed] [Google Scholar]
- 113.Stančák A, Riml A, Pfurtscheller G. The effects of external load on movement-related changes of the sensorimotor EEG rhythms. Electroencephalogr Clin Neurophysiol. (1997) 102:495–504. 10.1016/S0013-4694(96)96623-0 [DOI] [PubMed] [Google Scholar]
- 114.Toma K, Mima T, Matsuoka T, Gerloff C, Ohnishi T, Koshy B, et al. Movement rate effect on activation and functional coupling of motor cortical areas. J Neurophysiol. (2002) 88:3377–85. 10.1152/jn.00281.2002 [DOI] [PubMed] [Google Scholar]
- 115.Hummel F, Kirsammer R, Gerloff C. Ipsilateral cortical activation during finger sequences of increasing complexity: representation of movement difficulty or memory load? Clin Neurophysiol. (2003) 114:605–13. 10.1016/S1388-2457(02)00417-0 [DOI] [PubMed] [Google Scholar]
- 116.Hall SD, Stanford IM, Yamawaki N, McAllister CJ, Rönnqvist KC, Woodhall GL, et al. The role of GABAergic modulation in motor function related neuronal network activity. Neuroimage (2011) 56:1506–10. 10.1016/j.neuroimage.2011.02.025 [DOI] [PubMed] [Google Scholar]
- 117.Muthukumaraswamy SD, Myers JFM, Wilson SJ, Nutt DJ, Lingford-Hughes A, Singh KD, et al. The effects of elevated endogenous GABA levels on movement-related network oscillations. Neuroimage (2012) 66C:36–41. 10.1016/j.neuroimage.2012.10.054 [DOI] [PubMed] [Google Scholar]
- 118.Little S, Pogosyan A, Kuhn AA, Brown P. Beta band stability over time correlates with Parkinsonian rigidity and bradykinesia. Exp Neurol. (2012) 236:383–8. 10.1016/j.expneurol.2012.04.024 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 119.Riva N, Falini A, Inuggi A, Gonzalez-Rosa JJ, Amadio S, Cerri F, et al. Cortical activation to voluntary movement in amyotrophic lateral sclerosis is related to corticospinal damage: electrophysiological evidence. Clin Neurophysiol. (2012) 123:1586–92. 10.1016/j.clinph.2011.12.013 [DOI] [PubMed] [Google Scholar]
- 120.Bizovičar N, Dreo J, Koritnik B, Zidar J. Decreased movement-related beta desynchronization and impaired post-movement beta rebound in amyotrophic lateral sclerosis. Clin Neurophysiol. (2014) 125:1689–99. 10.1016/j.clinph.2013.12.108 [DOI] [PubMed] [Google Scholar]
- 121.Cheyne DO. MEG studies of sensorimotor rhythms: a review. Exp Neurol. (2013) 245:27–39. 10.1016/j.expneurol.2012.08.030 [DOI] [PubMed] [Google Scholar]
- 122.Proudfoot M, Rohenkohl G, Quinn A, Colclough GL, Wuu J, Talbot K, et al. Altered cortical beta-band oscillations reflect motor system degeneration in amyotrophic lateral sclerosis. Hum Brain Mapp. (2017) 38:237–54. 10.1002/hbm.23357 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 123.Vucic S, Ziemann U, Eisen A, Hallett M, Kiernan MC. Transcranial magnetic stimulation and amyotrophic lateral sclerosis: pathophysiological insights. J Neurol Neurosurg Psychiatry (2013) 84:1161–70. 10.1136/jnnp-2012-304019 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 124.Turner MR, Hammers A, Al-Chalabi A, Shaw CE, Andersen PM, Brooks DJ, et al. Distinct cerebral lesions in sporadic and “D90A” SOD1 ALS: studies with [11C]flumazenil PET. Brain (2005) 128:1323–9. 10.1093/brain/awh509 [DOI] [PubMed] [Google Scholar]
- 125.Baker SN, Kilner JM, Pinches EM, Lemon RN. The role of synchrony and oscillations in the motor output. Exp. Brain Res. 128, 109–17. 10.1007/s002210050825 [DOI] [PubMed] [Google Scholar]
- 126.Proudfoot M, van Ede F, Quinn A, Colclough GL, Wuu J, Talbot K, et al. Impaired corticomuscular and interhemispheric cortical beta oscillation coupling in amyotrophic lateral sclerosis. Clin Neurophysiol. (2018) 129:1479–89. 10.1016/j.clinph.2018.03.019 [DOI] [PubMed] [Google Scholar]
- 127.Kristeva R, Patino L, Omlor W. Beta-range cortical motor spectral power and corticomuscular coherence as a mechanism for effective corticospinal interaction during steady-state motor output. Neuroimage (2007) 36:785–92. 10.1016/j.neuroimage.2007.03.025 [DOI] [PubMed] [Google Scholar]
- 128.Fisher KM, Zaaimi B, Williams TL, Baker SN, Baker MR. Beta-band intermuscular coherence: a novel biomarker of upper motor neuron dysfunction in motor neuron disease. Brain (2012) 135:2849–64. 10.1093/brain/aws150 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 129.Hanagasi HA, Gurvit IH, Ermutlu N, Kaptanoglu G, Karamursel S, Idrisoglu HA, et al. Cognitive impairment in amyotrophic lateral sclerosis: evidence from neuropsychological investigation and event-related potentials. Cogn Brain Res. (2002) 14:234–44. 10.1016/S0926-6410(02)00110-6 [DOI] [PubMed] [Google Scholar]
- 130.Pekkonen E, Osipova D, Laaksovirta H. Magnetoencephalographic evidence of abnormal auditory processing in amyotrophic lateral sclerosis with bulbar signs. Clin Neurophysiol. (2004) 115:309–15. 10.1016/S1388-2457(03)00360-2 [DOI] [PubMed] [Google Scholar]
- 131.Raggi A, Consonni M, Iannaccone S, Perani D, Zamboni M, Sferrazza B, et al. Auditory event-related potentials in non-demented patients with sporadic amyotrophic lateral sclerosis. Clin Neurophysiol. (2008) 119:342–50. 10.1016/j.clinph.2007.10.010 [DOI] [PubMed] [Google Scholar]
- 132.Mannarelli D, Pauletti C, Locuratolo N, Vanacore N, Frasca V, Trebbastoni A, et al. Attentional processing in bulbar- and spinal-onset amyotrophic lateral sclerosis: insights from event-related potentials. Amyotroph Lateral Scler Frontotemporal Degener. (2014) 15:30–8. 10.3109/21678421.2013.787628 [DOI] [PubMed] [Google Scholar]
- 133.Volpato C, Piccione F, Silvoni S, Cavinato M, Palmieri A, Meneghello F, et al. Working memory in amyotrophic lateral sclerosis: auditory event-related potentials and neuropsychological evidence. J Clin Neurophysiol. (2010) 27:198–206. 10.1097/WNP.0b013e3181e0aa14 [DOI] [PubMed] [Google Scholar]
- 134.Zaehle T, Becke A, Naue N, Machts J, Abdulla S, Petri S, et al. Working memory in ALS patients: preserved performance but marked changes in underlying neuronal networks. PLoS ONE (2013) 8:e71973. 10.1371/journal.pone.0071973 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 135.Pinkhardt EH, Jürgens R, Becker W, Mölle M, Born J, Ludolph AC, et al. Signs of impaired selective attention in patients with amyotrophic lateral sclerosis. J Neurol. (2008) 255:532–8. 10.1007/s00415-008-0734-9 [DOI] [PubMed] [Google Scholar]
- 136.Volpato C, Prats Sedano MA, Silvoni S, Segato N, Cavinato M, Merico A, et al. Selective attention impairment in amyotrophic lateral sclerosis. Amyotroph Lateral Scler Front Degener. (2016) 17:236–44. 10.3109/21678421.2016.1143514 [DOI] [PubMed] [Google Scholar]
- 137.Seer C, Fürkötter S, Vogts M-B, Lange F, Abdulla S, Dengler R, et al. Executive dysfunctions and event-related brain potentials in patients with amyotrophic lateral sclerosis. Front Aging Neurosci. (2015) 7:225. 10.3389/fnagi.2015.00225 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 138.Seer C, Joop M, Lange F, Lange C, Dengler R, Petri S, et al. Attenuated error-related potentials in amyotrophic lateral sclerosis with executive dysfunctions. Clin Neurophysiol. (2017) 128:1496–503. 10.1016/j.clinph.2017.05.007 [DOI] [PubMed] [Google Scholar]
- 139.Goldstein LH, Abrahams S. Changes in cognition and behaviour in amyotrophic lateral sclerosis: nature of impairment and implications for assessment. Lancet Neurol. (2013) 12:368–80. 10.1016/S1474-4422(13)70026-7 [DOI] [PubMed] [Google Scholar]
- 140.Lange F, Lange C, Joop M, Seer C, Dengler R, Kopp B, et al. Neural correlates of cognitive set shifting in amyotrophic lateral sclerosis. Clin Neurophysiol. (2016) 127:3537–45. 10.1016/j.clinph.2016.09.019 [DOI] [PubMed] [Google Scholar]
- 141.Blain-Moraes S, Mashour GA, Lee H, Huggins JE, Lee U. Altered cortical communication in amyotrophic lateral sclerosis. Neurosci Lett. (2013) 543:172–6. 10.1016/j.neulet.2013.03.028 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 142.Gallassi R, Montagna P, Morreale A, Lorusso S, Tinuper P, Daidone R, et al. Neuropsychological, electroencephalogram and brain computed tomography findings in motor neuron disease. Eur Neurol. (1989) 29:115–20. 10.1159/000116391 [DOI] [PubMed] [Google Scholar]
- 143.Mai R, Facchetti D, Micheli A, Poloni M. Quantitative electroencephalography in amyotrophic lateral sclerosis. Electroencephalogr Clin Neurophysiol. (1998) 106:383–6. 10.1016/S0013-4694(97)00159-4 [DOI] [PubMed] [Google Scholar]
- 144.Santhosh J, Bhatia M, Sahu S, Anand S. Decreased electroencephalogram alpha band [8-13 Hz] power in amyotrophic lateral sclerosis patients: a study of alpha activity in an awake relaxed state. Neurol India (2005) 53:99–101. 10.4103/0028-3886.15071 [DOI] [PubMed] [Google Scholar]
- 145.Jayaram V, Widmann N, Förster C, Fomina T, Hohmann M, Müller Vom Hagen J, et al. Brain-computer interfacing in amyotrophic lateral sclerosis: Implications of a resting-state EEG analysis. Conf Proc. (2015) 2015:6979–82. 10.1109/EMBC.2015.7319998 [DOI] [PubMed] [Google Scholar]
- 146.Kopell NJ, Gritton HJ, Whittington MA, Kramer MA. Beyond the connectome: the dynome. Neuron (2014) 83:1319–28. 10.1016/j.neuron.2014.08.016 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 147.Iyer PM, Egan C, Pinto-Grau M, Burke T, Elamin M, Nasseroleslami B, et al. Functional connectivity changes in resting-state EEG as potential biomarker for amyotrophic lateral sclerosis. PLoS ONE (2015) 10:e0128682. 10.1371/journal.pone.0128682 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 148.Nasseroleslami B, Dukic S, Broderick M, Mohr K, Schuster C, Gavin B, et al. Characteristic increases in EEG connectivity correlate with changes of structural MRI in amyotrophic lateral sclerosis. Cereb Cortex (2017) 29:27–41. 10.1093/cercor/bhx301 [DOI] [PubMed] [Google Scholar]
- 149.Fraschini M, Demuru M, Hillebrand A, Cuccu L, Porcu S, Di Stefano F, et al. EEG functional network topology is associated with disability in patients with amyotrophic lateral sclerosis. Sci Rep. (2016) 6:38653. 10.1038/srep38653 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 150.Fraschini M, Lai M, Demuru M, Puligheddu M, Floris G, Borghero G, et al. Functional brain connectivity analysis in amyotrophic lateral sclerosis: an EEG source-space study. Biomed Phys Eng Express (2018) 4:037004 10.1088/2057-1976/aa9c64 [DOI] [Google Scholar]
- 151.Proudfoot M, Colclough GL, Quinn A, Wuu J, Talbot K, Benatar M, et al. Increased cerebral functional connectivity in ALS: a resting-state magnetoencephalography study. Neurology (2018) 90:e1418–24. 10.1212/WNL.0000000000005333 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 152.Loewe K, Machts J, Kaufmann J, Petri S, Heinze H-J, Borgelt C, et al. Widespread temporo-occipital lobe dysfunction in amyotrophic lateral sclerosis. Sci Rep. (2017) 7:40252. 10.1038/srep40252 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 153.Höller Y, Uhl A, Bathke A, Thomschewski A, Butz K, Nardone R, et al. Reliability of EEG measures of interaction: a paradigm shift is needed to fight the reproducibility crisis. Front Hum Neurosci. (2017) 11:1–15. 10.3389/fnhum.2017.00441 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 154.Sorrentino P, Rucco R, Jacini F, Trojsi F, Lardone A, Baselice F, et al. Brain functional networks become more connected as amyotrophic lateral sclerosis progresses: a source level magnetoencephalographic study. NeuroImage Clin. (2018) 20:564–71. 10.1016/j.nicl.2018.08.001 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 155.Cope TE, Sohoglu E, Sedley W, Patterson K, Jones PS, Wiggins J, et al. Evidence for causal top-down frontal contributions to predictive processes in speech perception. Nat Commun. (2017) 8:2154. 10.1038/s41467-017-01958-7 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 156.Baillet S. Magnetoencephalography for brain electrophysiology and imaging. Nat Neurosci. (2017) 20:327–39. 10.1038/nn.4504 [DOI] [PubMed] [Google Scholar]
- 157.Boto E, Holmes N, Leggett J, Roberts G, Shah V, Meyer SS, et al. Moving magnetoencephalography towards real-world applications with a wearable system. Nature (2018) 555:657–61. 10.1038/nature26147 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 158.Bede P, Bokde ALW, Byrne S, Elamin M, Fagan AJ, Hardiman O. Spinal cord markers in ALS: diagnostic and biomarker considerations. Amyotroph Lateral Scler. (2012) 13:407–15. 10.3109/17482968.2011.649760 [DOI] [PubMed] [Google Scholar]
- 159.Lawrence J, Stroman PW, Malisza KL. Functional MRI of the cervical spinal cord during noxious and innocuous thermal stimulation in the alpha-chloralose- and halothane-anesthetized rat. Magn Reson Imaging (2008) 26:1–10. 10.1016/j.mri.2007.05.001 [DOI] [PubMed] [Google Scholar]
- 160.Malisza KL, Stroman PW. Functional imaging of the rat cervical spinal cord. J Magn Reson Imaging (2002) 16:553–8. 10.1002/jmri.10185 [DOI] [PubMed] [Google Scholar]
- 161.Stroman PW, Krause V, Malisza KL, Frankenstein UN, Tomanek B. Functional magnetic resonance imaging of the human cervical spinal cord with stimulation of different sensory dermatomes. Magn Reson Imaging (2002) 20:1–6. 10.1016/S0730-725X(02)00468-X [DOI] [PubMed] [Google Scholar]
- 162.Kornelsen J, Stroman PW. fMRI of the lumbar spinal cord during a lower limb motor task. Magn Reson Med. (2004) 52:411–4. 10.1002/mrm.20157 [DOI] [PubMed] [Google Scholar]
- 163.Agosta F, Valsasina P, Caputo D, Stroman PW, Filippi M. Tactile-associated recruitment of the cervical cord is altered in patients with multiple sclerosis. Neuroimage (2008) 39:1542–8. 10.1016/J.NEUROIMAGE.2007.10.048 [DOI] [PubMed] [Google Scholar]
- 164.Stroman PW, Kornelsen J, Bergman A, Krause V, Ethans K, Malisza KL, et al. Noninvasive assessment of the injured human spinal cord by means of functional magnetic resonance imaging. Spinal Cord. (2004) 42:59–66. 10.1038/sj.sc.3101559 [DOI] [PubMed] [Google Scholar]
- 165.Eippert F, Kong Y, Winkler AM, Andersson JL, Finsterbusch J, Büchel C, et al. Investigating resting-state functional connectivity in the cervical spinal cord at 3T. Neuroimage (2017) 147:589–601. 10.1016/j.neuroimage.2016.12.072 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 166.Zhang Y, Fang T, Wang Y, Guo X, Alarefi A, Wang J, et al. Occipital cortical gyrification reductions associate with decreased functional connectivity in amyotrophic lateral sclerosis. Brain Imaging Behav. (2017) 11:1–7. 10.1007/s11682-015-9499-9 [DOI] [PubMed] [Google Scholar]
- 167.Bueno APA, Pinaya WHL, Moura LM, Bertoux M, Radakovic R, Kiernan MC, et al. Structural and functional papez circuits integrity in amyotrophic lateral sclerosis. Brain Imaging Behav. (2018) 12:1622–30. 10.1007/s11682-018-9825-0 [DOI] [PubMed] [Google Scholar]