Amyotrophic Lateral Sclerosis and Novel Therapeutic StrategiesView this Special Issue
Review Article | Open Access
Katja Kollewe, Sonja Körner, Reinhard Dengler, Susanne Petri, Bahram Mohammadi, "Magnetic Resonance Imaging in Amyotrophic Lateral Sclerosis", Neurology Research International, vol. 2012, Article ID 608501, 9 pages, 2012. https://doi.org/10.1155/2012/608501
Magnetic Resonance Imaging in Amyotrophic Lateral Sclerosis
Amyotrophic lateral sclerosis (ALS) is a rapidly progressing neurodegenerative disorder which is incurable to date. As there are many ongoing studies with therapeutic candidates, it is of major interest to develop biomarkers not only to facilitate early diagnosis but also as a monitoring tool to predict disease progression and to enable correct randomization of patients in clinical trials. Magnetic resonance imaging (MRI) has made substantial progress over the last three decades and is a practical, noninvasive method to gain insights into the pathology of the disease. Disease-specific MRI changes therefore represent potential biomarkers for ALS. In this paper we give an overview of structural and functional MRI alterations in ALS with the focus on task-free resting-state investigations to detect cortical network failures.
Amyotrophic lateral sclerosis (ALS) is a devastating neurodegenerative disease which affects not only motor function but also involves extramotor systems. According to the revised El Escorial criteria for the diagnosis of ALS the presence of signs for the affection of both upper motor neurons (UMN) in the primary motor cortex and lower motor neurons (LMN) in brain stem and spinal cord is mandatory, and the disease must be progressive . ALS, has a wide variety of clinical phenotypes, and it is therefore sometimes difficult to differentiate ALS from other ALS-mimicking conditions. For the detection of LMN involvement in different body regions, electromyography (EMG) can be used in addition to the clinical examination. UMN signs, on the other hand, must be visible at the clinical examination while electrophysiological transcranial motor stimulation (TMS) abnormalities are not accepted for the diagnosis of ALS according to the El Escorial criteria. Therefore it would be very advantageous to have an additional technical method which sensitively monitors UMN involvement. Magnetic resonance imaging (MRI), and here in particular diffusion tensor imaging (DTI), represents a promising technique for early detection of alterations in the motor cortex and pyramidal tracts. Different other MRI techniques are also currently being developed to serve as biomarkers for earlier and more accurate diagnosis of ALS. A biomarker for UMN affection would further be useful to monitor the neurodegenerative process and therefore disease progression, that is, within clinical trials. Guidelines for the use of neuroimaging in the management of motor neuron diseases have recently been published by the European Federation of Neurological Societies (EFNS) . In 2010, an international group of experts has met at Oxford University, UK, to define essential parameters for future research needed to promote MRI as a biomarker for ALS.
It was concordantly proposed to initiate longitudinal and multicenter studies and thus to analyze larger sample sizes so that results can be optimized and MRI can become a more specific diagnostic tool. Within MRI, one must distinguish between structural and functional MRI techniques. Structural MRI detects morphological changes in grey and white matter. The diffusion tensor imaging method can be used for tractography (e.g., imaging of pyramidal tracts) or to study the connection between different cortical grey matter areas. Structural MRI at present mainly serves to rule out other diseases mimicking ALS but is also supposed to be useful in finding cortical atrophy in ALS. Functional MRI (fMRI) can detect cortical activations corresponding to a task (e.g., motor task) performed by the participant during scanning. The resting-state technique detects fluctuations in different cortical areas during rest (no task performance needed) and visualizes different functional networks such as the sensori-motor network, visual network, and others. FMRI methods are therefore capable of detecting ALS-related differences in brain activation, compensation, and reorganisation.
This paper describes the structural and functional MRI alterations which have been found in ALS to date, with a particular focus on task-free resting-state investigations to detect cortical network failures.
2. Structural Magnetic Resonance Imaging
2.1. Voxel-Based Morphometry
The voxel-based morphometry (VBM) technique can be used for the analysis of volumetric changes in gray or white matter (GM; WM) in the brain [3–5]. It is an automated analysis of changes in brain volume using high-resolution three-dimensional T1-weighted MRI scans. During the statistical process, potential structural changes in individual patients are compared to a template of age-matched controls. By this approach, neuroanatomical differences can be detected with much greater sensitivity [2, 6–8].
2.1.1. Motor Cortex
Several VBM studies have described atrophy of the primary motor cortex (Figure 1) but this has surprisingly not been a consistent finding in all studies published so far [3, 4, 9, 10]. Marked decreases in the grey matter in the bilateral paracentral lobule were also detected, indicating that the premotor cortex is also involved in degenerative processes in ALS .
2.1.2. Extra-Motor Involvement
Regional gray matter loss measured by VBM extends to the frontal, temporal, parietal, occipital and limbic regions of the brain and has also been described for the corpus callosum and the cerebellum which is line with clinical and neuroanatomical data [3, 5, 10, 12–14].
3. Diffusion Tensor Imaging (DTI)
It is known from postmortem studies in ALS brain specimens that there are extensive white matter abnormalities in the region of the central sulcus and the corticospinal tract (CST), extending across the corpus callosum and into the frontal lobes [15, 16].
To investigate white matter and the directionality of fiber tracts, diffusion tensor imaging (DTI) detects alterations in the degree (axial diffusivity, AD) and directedness (fractional anisotropy, FA) of proton movement. It is sensitive to the direction of water movement in vivo. As the diffusion properties of water molecules, demonstrated by DTI, are restricted by the presence of barriers (e.g., cellular membranes), the water molecules tend to diffuse preferentially in orientations along axons, leading to an anisotropic diffusion. Therefore, DTI is used to detect pathology within neuronal white matter tracts and reflects microstructural tissue changes . FA is reduced with loss of neuronal pathway integrity; mean diffusivity (MD) is increased with a loss of neuronal pathway integrity .
3.1. Corticospinal Tract (CST)
The CST is the structure most frequently studied by DTI in ALS [3, 4, 17] and decreased fractional anisotropy (FA) values in this area have consistently been reported [18–22]. To date, correlation of disease severity and decreased FA has been controversially debated, as some studies found a relation between these factors [20, 23, 24] whereas others did not [25–27]. It is also discussed controversially if increased mean diffusivity (MD) correlates with disease duration, as this was reported by [24, 26, 28], but not by other authors . One other group demonstrated a correlation of a lower mean FA in the CST with rapid disease progression .
3.2. Corpus Callosum (CC)
Neuropathological studies have shown involvement of the corpus callosum (CC) in ALS [15, 16] and so did several DTI studies, which observed FA changes within the CC of ALS patients [30–36]. The largest FA changes were observed in the posterocentral portion of the CC which is known to link the two motor cortices [9, 16]. The involvement of the CC at an early disease stage would be in line with recent clinical studies [37, 38] and provide an explanation for the focal onset followed by a rapidly spreading progression of the disease.
Unfortunately, the changes in the CC are not specific and were also found in patients with other diseases of the upper motor neuron such as hereditary spastic paraparesis [39, 40] while not detectable in a lower motor neuron syndrome as Kennedy’s disease .
3.3. Extramotor Involvement
3.4. Spinal Cord
The small diameter of the spinal cord and its surroundings and breathing-mediated movement artefacts make it difficult to investigate the spinal cord by DTI . In one study, the cervical cord has been investigated and compared to controls; ALS patients showed significantly lower FA of the cervical cord while MD did not differ between the two groups . But during the course of the disease (9 months followup) , FA showed a significant decrease and MD showed a significant increase in the spinal cord of ALS patients. A further study supports the hypothesis that the degenerative process in ALS is mostly a “dying-back” mechanism, as the distal part of the spinal cord was the most altered one [7, 44].
3.5. Summary I (Structural MRI)
According to the consensus guidelines on MRI protocols for studies in ALS patients, DTI is the most promising structural MRI method to detect ALS-related changes not only in the primary motor cortex and the pyramidal tracts but also in brain regions beyond the motor system. DTI scans with a minimum of 12 gradient directions (isotropic voxels with a maximum of 2.5 mm slice thickness) have previously been recommended , although, especially for longitudinal studies, 20–30 directions would be preferable in order to permit robust diffusivity measurements . Studies in larger patient cohorts and repeated measurements in the same patients throughout disease progression are necessary to develop DTI as a potential biomarker for preclinical UMN involvement or as a tool to monitor disease progression and the response to therapy in ALS. Beside FA and MD, measuring the strength of connectivity between different anatomical clusters of grey matter can reveal alterations in cortical networks in ALS patients compared to healthy controls. Using DTI one can calculate the connectivity between cortical areas as shown in the following figure (Figure 2, ). This novel approach may contribute to an increase in sensitivity and specificity of DTI in ALS.
(b) NBS. Threshold
4. Functional Magnetic Resonance Imaging
Functional MRI (fMRI) means the visualization of brain regions in action and is typically done using BOLD-weighted MRI.
BOLD—fMRI takes advantages of the oxygenation level of blood, which is different during rest and activity of the brain when the brain is active, despite the increase in oxygen consumption, there is a subsequent increase in local blood flow that paradoxically results in a decrease of concentration of deoxygenated haemoglobin in the local microvasculature of the activated region. Oxygenated hemoglobin is weakly diamagnetic, while deoxygenated hemoglobin is strongly paramagnetic, thus an increase in the relative concentration of oxygenated hemoglobin results in a lengthened T2*, giving an increase in local MRI signal for T2*-weighted MRI.
This change leads to an increase in the fMRI signal approximately 4 seconds after the neural event in the brain. Thereafter, an equilibration of oxy- and deoxyhemoglobin succeeds the “deactivation phase.” This contrast alone is too weak to show differences to the surrounding brain regions. Comparisons with the same region at rest have to be done followed by special analysis methods [6, 47–49]. The advantages of the BOLD technique are evident: it is noninvasive, provides high resolution, and has a wide accessibility.
Studies with fMRI using a motor task have shown increased cortical activity in ALS patients in the ipsi- and contralateral sensorimotor cortex, supplementary motor area, basal ganglia, and cerebellum [50–53]. This has been discussed as being either the result of cortical adaptation due to peripheral weakness  or of cortical reorganisation . In a recent study we have demonstrated that increased cortical activation can be detected even when the performing hand was clinically not affected and interpreted this as a sign of cortical reorganisation in clinically early stages of disease. In this study we could show that early and late phases of neuroplastic changes in ALS can be distinguished according to different disease stages . In another fMRI study we have described for the first time that the pattern of cortical activation during tongue movements differs in ALS patients with and without bulbar signs . We have further investigated this finding by repeated measurements during disease progression in ALS patients with limb and/or bulbar signs, using two different motor tasks (vertical tongue movement and movement of the right hand). In this study, we detected two different patterns of cortical activation changes which were dependent on the presence or absence of bulbar signs. This observation suggests fundamental differences in the neurodegenerative process and subsequent reorganisation mechanisms according to the affected body regions, which apparently can exist in parallel in the same patients .
As it is difficult to control task performance in patients with motor deficits, the analysis of “functional connectivity” of spatially remote brain regions has recently gained increasing interest in neuroimaging research in ALS. The idea is that during rest spontaneous coherent fluctuations of the BOLD signal exist in different brain areas which are functionally connected . Resting-state imaging of discrete cortical networks provides a new technique to explore ALS as system failure of interconnected networks . This method only take minutes to acquire and does not suffer from performance confounds that may be present in patients with cognitive or motor impairments [58–60]. It is therefore more suitable for clinical use and in particular for multicentre studies.
There are different typical resting-state networks which can be recovered from the BOLD signal with high reliability across individuals and studies (Figure 3) [58, 61–63]. One of the consistently recovered networks is the default-mode network (DMN) which is conceptualized as a stand alone cognitive network [64, 65]. It comprises a large frontal area including the ventral anterior cingulate cortex (vACC), the medial prefrontal cortex (MPFC) and the orbitofrontal cortex (OFC)), the posterior cingulate cortex (PCC), the inferior parietal cortex (IPC), and one temporal region, the parahippocampal gyrus (PHG) [62, 66, 67]. Another often reported network is the sensorimotor network [58, 61, 62] which includes the primary motor cortex (PMC), the anterior part of the cingulate cortex (ACC), the somatosensory region (SSC), and the auditory cortex (Aud. C) [62, 66–68]. In addition, several other networks such as a visual executive network have been described [58, 61, 62].
(a) Default-mode network
(b) Sensori-motor network
(c) Fronto-temp.-par. network
(d) Ventral network
ALS is a neurodegenerative disease which involves mainly the motor system, but already early descriptions  and more recent neuropsychological [71–74], electrophysiological [75–77], neuropathological , and neuroimaging [78–80] studies pointed out that other than the motor regions of the nervous system are involved in the degenerative process.
We analyzed for the first time the resting-state networks in ALS patients . Given the definition of ALS as a motor neuron disease, we expected most prominent differences between ALS patients and healthy controls in the sensori-motor network. In view of the increasing knowledge about extramotor involvement in ALS as described above, we also suspected differences between ALS patients and healthy controls in the default-mode network.
We investigated 20 patients suffering from ALS and 20 healthy age-matched controls in a 3-Tesla Siemens Magnetom Allegra Scanner (Erlangen, Germany). The first group consisted of 20 patients, who fulfilled the diagnostic criteria for probable or definite ALS during the course of the disease according to the revised El Escorial criteria of the World Federation of Neurology . The control group comprised 20 healthy volunteers. During the data acquisition for functional connectivity the subjects were instructed to neither engage in cognitive nor motor activity. Analysis and visualization of the data were performed using BrainVoyager QX (Brain Innovation BV, Maastricht, The Netherlands) software.
Applying independent component analysis (ICA), different robustly reproducible functional networks could be extracted from the resting state in both groups [81–85]. Only in two networks, the default-mode and the sensori-motor networks, we found significant differences between ALS patients and healthy controls.
4.1. Default-Mode Network (DMN)
This network has received considerable attention over the past few years. In the study presented here, we found distinct differences of the default-mode network comparing healthy subjects with ALS patients; in ALS patients we found a significantly decreased connectivity in the lateral prefrontal cortex (BA9), PCC (BA 23), and IPC (BA39) (Figure 4). The PCC, MPFC, and the bilateral IPC are seen as “core hub” of this network and showed a strong intraregional correlation with each other and a weaker correlation with the remaining regions such as the temporal cortex and the medial temporal lobe [57, 86]. Considering our data, functional connectivity is decreased in the core hub of the default-mode network in ALS patients without affecting subcortical (PHG) or temporal regions. In the prefrontal region we found decreased connectivity in BA 9 which is typically involved in working memory tasks, in tasks of sustained attention, and (bilaterally) in tasks demanding problem solving . In IPC we found reduced connectivity in ALS patients located in BA 39. Left BA 39 is known to be involved in perception, recognition, and recall of written language as well as in problem solving .
(c) Healthy > ALS
All in all, the particular pattern of differences between ALS and control subjects for the default mode network (DMN) bodes well with previous neuropsychological studies suggesting an impairment of higher level executive functions [72–74, 89–91].
4.2. Sensori-Motor Network
Regarding the sensori-motor network, our study detected differences between ALS patients and controls only in the premotor area (BA6) (Figure 5). All other regions of the sensori-motor network, in particular the precentral gyrus, did not show significant differences between the two groups. Alterations in premotor cortex activity have been demonstrated in a number of functional imaging studies in ALS [50–53, 92, 93] but it has been discussed that these changes might be due to the fact that the same task might be more difficult for ALS patients (and hence associated with increased activation) rather than due to genuine functional changes. As in the present approach no task is imposed on the subject, our data favour a primary functional involvement of the premotor cortex in ALS.
(c) Healthy > ALS
Taken together, we demonstrated significant changes in the DMN and the sensori-motor network. This suggests a disease-specific alteration of these two networks. The DMN has been linked to cognitive processes whereas the latter has been shown to be involved in motor control. The present results once again support extra-motor involvement in ALS.
In the mean time, several further studies have addressed the issue of functional connectivity in ALS by analysis of resting-state data, with partially conflicting results. In a study in 20 ALS patients and healthy age-matched controls, alterations in sensori-motor network in the ALS patient group were detected, similar to our data, but significant changes were seen only in a subgroup of ALS-patients in the DMN, and in the right frontoparietal network .
In another study, 25 ALS-patients and age-matched healthy controls were investigated using a combined method to study both structural and functional connectivity . This integrated approach identified apparently dichotomous processes characterizing the cerebral network failure in ALS with increased functional connectivity within regions of decreased structural connectivity. This may point out an interaction between functional and structural connectivity. The atrophy of white matter between distinct cortical areas (reduced structural connectivity) correlates to a higher synchronization of BOLD and an increased interplay between these areas (functional connectivity).
In a third study by the so-called “seed-based analysis” no significant changes in the functional connectivity of the motor system were seen . By this method one searches for a simple correlation between two predefined areas. These partially controversial results of the different studies which investigated resting-state network changes in ALS might be due to methodological differences or to different compositions of the patient groups with different grades of disease severity and therefore different pathological stages. As recently recommended , further studies with greater numbers of patients including sufficient numbers of patients in different disease stages could provide better insight into changes of the distinct cerebral networks and their relation to the disease process. In the future, it will be important to pursue multimodal approaches looking for grey matter changes, structural connectivity and functional connectivity, and their correlation with different clinical scores (ALSFRS, neuropsychological parameters, motor performance).
Of the currently available structural and functional MRI techniques, a combination of DTI and resting fMRI might provide the most promising early screening protocol to identify subjects “at risk” for developing ALS. However, further validation studies in larger patients’ samples are required before these techniques can enter the clinical routine .
- B. R. Brooks, R. G. Miller, M. Swash, and T. L. Munsat, “El Escorial revisited: revised criteria for the diagnosis of amyotrophic lateral sclerosis,” Amyotrophic Lateral Sclerosis, vol. 1, no. 5, pp. 293–299, 2000.
- M. Filippi, F. Agosta, S. Abrahams et al., “EFNS guidelines on the use of neuroimaging in the management of motor neuron diseases,” European Journal of Neurology, vol. 17, no. 4, pp. 526–e20, 2010.
- F. Agosta, E. Pagani, M. A. Rocca et al., “Voxel-based morphometry study of brain volumetry and diffusivity in amyotrophic lateral sclerosis patients with mild disability,” Human Brain Mapping, vol. 28, no. 12, pp. 1430–1438, 2007.
- J. Grosskreutz, J. Kaufmann, J. Frädrich, R. Dengler, H. J. Heinze, and T. Peschel, “Widespread sensorimotor and frontal cortical atrophy in amyotrophic lateral sclerosis,” BMC Neurology, vol. 6, article 17, 2006.
- J. L. Chang, C. Lomen-Hoerth, J. Murphy et al., “A voxel-based morphometry study of patterns of brain atrophy in ALS and ALS/FTLD,” Neurology, vol. 65, no. 1, pp. 75–80, 2005.
- S. Wang, E. R. Melhem, H. Poptani, and J. H. Woo, “Neuroimaging in amyotrophic lateral sclerosis,” Neurotherapeutics, vol. 8, no. 1, pp. 63–71, 2011.
- M. R. Turner and M. Modo, “Advances in the application of MRI to amyotrophic lateral sclerosis,” Expert Opinion on Medical Diagnostics, vol. 4, no. 6, pp. 483–496, 2010.
- X.-Q. Ding, K. Kollewe, K. Blum et al., “Value of quantitative analysis of routine clinical MRI sequences in ALS,” Amyotrophic Lateral Sclerosis, vol. 12, no. 6, pp. 406–413, 2011.
- N. Filippini, G. Douaud, C. E. MacKay, S. Knight, K. Talbot, and M. R. Turner, “Corpus callosum involvement is a consistent feature of amyotrophic lateral sclerosis,” Neurology, vol. 75, no. 18, pp. 1645–1652, 2010.
- D. M. Mezzapesa, A. Ceccarelli, F. Dicuonzo et al., “Whole-brain and regional brain atrophy in amyotrophic lateral sclerosis,” American Journal of Neuroradiology, vol. 28, no. 2, pp. 255–259, 2007.
- J. Senda, S. Kato, T. Kaga et al., “Progressive and widespread brain damage in ALS: MRI voxel-based morphometry and diffusion tensor imaging study,” Amyotrophic Lateral Sclerosis, vol. 12, no. 1, pp. 59–69, 2011.
- S. Abrahams, L. H. Goldstein, J. Suckling et al., “Frontotemporal white matter changes in amyotrophic lateral sclerosis,” Journal of Neurology, vol. 252, no. 3, pp. 321–331, 2005.
- J. Kassubek, A. Unrath, H. J. Huppertz et al., “Global brain atrophy and corticospinal tract alterations in ALS, as investigated by voxel-based morphometry of 3-D MRI,” Amyotrophic Lateral Sclerosis and Other Motor Neuron Disorders, vol. 6, no. 1, pp. 213–220, 2005.
- S. Petri, K. Kollewe, C. Grothe et al., “GABAA-receptor mRNA expression in the prefrontal and temporal cortex of ALS patients,” Journal of the Neurological Sciences, vol. 250, no. 1-2, pp. 124–132, 2006.
- B. Brownell, D. R. Oppenheimer, and J. T. Hughes, “The central nervous system in motor neurone disease,” Journal of Neurology Neurosurgery and Psychiatry, vol. 33, no. 3, pp. 338–357, 1970.
- M. C. Smith, “Nerve fibre degeneration in the brain in amyotrophic lateral sclerosis,” Journal of Neurology, Neurosurgery, and Psychiatry, vol. 23, no. 4, pp. 269–282, 1960.
- M. R. Turner, M. C. Kiernan, P. N. Leigh, and K. Talbot, “Biomarkers in amyotrophic lateral sclerosis,” The Lancet Neurology, vol. 8, no. 1, pp. 94–109, 2009.
- P. Valsasina, F. Agosta, B. Benedetti et al., “Diffusion anisotropy of the cervical cord is strictly associated with disability in amyotrophic lateral sclerosis,” Journal of Neurology, Neurosurgery and Psychiatry, vol. 78, no. 5, pp. 480–484, 2007.
- G. Nair, J. D. Carew, S. Usher, D. Lu, X. P. Hu, and M. Benatar, “Diffusion tensor imaging reveals regional differences in the cervical spinal cord in amyotrophic lateral sclerosis,” NeuroImage, vol. 53, no. 2, pp. 576–583, 2010.
- S. Wang, H. Poptani, J. H. Woo et al., “Amyotrophic lateral sclerosis: diffusion-tensor and chemical shift MR imaging at 3.0 T,” Radiology, vol. 239, no. 3, pp. 831–838, 2006.
- C. Pohl, W. Block, J. Karitzky et al., “Proton magnetic resonance spectroscopy of the motor cortex in 70 patients with amyotrophic lateral sclerosis,” Archives of Neurology, vol. 58, no. 5, pp. 729–735, 2001.
- J. Suhy, R. G. Miller, R. Rule et al., “Early detection and longitudinal changes in amyotrophic lateral sclerosis by 1H MRSI,” Neurology, vol. 58, no. 5, pp. 773–779, 2002.
- J. M. Graham, N. Papadakis, J. Evans et al., “Diffusion tensor imaging for the assessment of upper motor neuron integrity in ALS,” Neurology, vol. 63, no. 11, pp. 2111–2119, 2004.
- C. M. Ellis, A. Simmons, D. K. Jones et al., “Diffusion tensor MRI assesses corticospinal tract damage in ALS,” Neurology, vol. 53, no. 5, pp. 1051–1058, 1999.
- H. Mitsumoto, A. M. Uluǧ, S. L. Pullman et al., “Quantitative objective markers for upper and lower motor neuron dysfunction in ALS,” Neurology, vol. 68, no. 17, pp. 1402–1410, 2007.
- A. T. Toosy, D. J. Werring, R. W. Orrell et al., “Diffusion tensor imaging detects corticospinal tract involvement at multiple levels in amyotrophic lateral sclerosis,” Journal of Neurology, Neurosurgery and Psychiatry, vol. 74, no. 9, pp. 1250–1257, 2003.
- S. K. Schimrigk, B. Bellenberg, M. Schlüter et al., “Diffusion tensor imaging-based fractional anisotropy quantification in the corticospinal tract of patients with amyotrophic lateral sclerosis using a probabilistic mixture model,” American Journal of Neuroradiology, vol. 28, no. 4, pp. 724–730, 2007.
- O. Abe, H. Yamada, Y. Masutani et al., “Amyotrophic lateral sclerosis: diffusion tensor tractography and voxel-based analysis,” NMR in Biomedicine, vol. 17, no. 6, pp. 411–416, 2004.
- O. Ciccarelli, T. E. Behrens, D. R. Altmann et al., “Probabilistic diffusion tractography: a potential tool to assess the rate of disease progression in amyotrophic lateral sclerosis,” Brain, vol. 129, no. 7, pp. 1859–1871, 2006.
- M. Sach, G. Winkler, V. Glauche et al., “Diffusion tensor MRI of early upper motor neuron involvement in amyotrophic lateral sclerosis,” Brain, vol. 127, no. 2, pp. 340–350, 2004.
- L. Thivard, P. F. Pradat, S. Lehéricy et al., “Diffusion tensor imaging and voxel based morphometry study in amyotrophic lateral sclerosis: relationships with motor disability,” Journal of Neurology, Neurosurgery and Psychiatry, vol. 78, no. 8, pp. 889–892, 2007.
- C. Bartels, N. Mertens, S. Hofer et al., “Callosal dysfunction in amyotrophic lateral sclerosis correlates with diffusion tensor imaging of the central motor system,” Neuromuscular Disorders, vol. 18, no. 5, pp. 398–407, 2008.
- J. Senda, M. Ito, H. Watanabe et al., “Correlation between pyramidal tract degeneration and widespread white matter involvement in amyotrophic lateral sclerosis: a study with tractography and diffusion-tensor imaging,” Amyotrophic Lateral Sclerosis, vol. 10, no. 5-6, pp. 288–294, 2009.
- C. A. Sage, W. van Hecke, R. Peeters et al., “Quantitative diffusion tensor imaging in amyotrophic lateral sclerosis: revisited,” Human Brain Mapping, vol. 30, no. 11, pp. 3657–3675, 2009.
- C. A. Sage, R. R. Peeters, A. Görner, W. Robberecht, and S. Sunaert, “Quantitative diffusion tensor imaging in amyotrophic lateral sclerosis,” NeuroImage, vol. 34, no. 2, pp. 486–499, 2007.
- O. Ciccarelli, T. E. Behrens, H. Johansen-Berg et al., “Investigation of white matter pathology in ALS and PLS using tract-based spatial statistics,” Human Brain Mapping, vol. 30, no. 2, pp. 615–624, 2009.
- J. M. Ravits and A. R. La Spada, “ALS motor phenotype heterogeneity, focality, and spread: deconstructing motor neuron degeneration,” Neurology, vol. 73, no. 10, pp. 805–811, 2009.
- S. Körner, K. Kollewe, M. Fahlbusch et al., “Onset and spreading patterns of upper and lower motor neuron symptoms in amyotrophic lateral sclerosis,” Muscle and Nerve, vol. 43, no. 5, pp. 636–642, 2011.
- S. M. Riad, H. Hathout, and J. C. Huang, “High T2 signal in primary lateral sclerosis supports the topographic distribution of fibers in the corpus callosum: assessing disease in the primary motor segment,” American Journal of Neuroradiology, vol. 32, no. 4, pp. E61–E64, 2011.
- M. C. Tartaglia, V. Laluz, A. Rowe et al., “Brain atrophy in primary lateral sclerosis,” Neurology, vol. 72, no. 14, pp. 1236–1241, 2009.
- A. Unrath, H. P. Müller, A. Riecker, A. C. Ludolph, A. D. Sperfeld, and J. Kassubek, “Whole brain-based analysis of regional white matter tract alterations in rare motor neuron diseases by diffusion tensor imaging,” Human Brain Mapping, vol. 31, no. 11, pp. 1727–1740, 2010.
- M. R. Turner, J. Grosskreutz, J. Kassubek et al., “Towards a neuroimaging biomarker for amyotrophic lateral sclerosis,” The Lancet Neurology, vol. 10, no. 5, pp. 400–403, 2011.
- F. Agosta, M. A. Rocca, P. Valsasina et al., “A longitudinal diffusion tensor MRI study of the cervical cord and brain in amyotrophic lateral sclerosis patients,” Journal of Neurology, Neurosurgery and Psychiatry, vol. 80, no. 1, pp. 53–55, 2009.
- D. G. Nair, S. Hutchinson, F. Fregni, M. Alexander, A. Pascual-Leone, and G. Schlaug, “Imaging correlates of motor recovery from cerebral infarction and their physiological significance in well-recovered patients,” NeuroImage, vol. 34, no. 1, pp. 253–263, 2007.
- D. K. Jones, “The effect of gradient sampling schemes on measures derived from diffusion tensor MRI: a Monte Carlo study,” Magnetic Resonance in Medicine, vol. 51, no. 4, pp. 807–815, 2004.
- E. Verstraete, J. H. Veldink, R. C. W. Mandl, L. H. van den Berg, and M. P. van den Heuvel, “Impaired structural motor connectome in amyotrophic lateral sclerosis,” PLoS ONE, vol. 6, no. 9, Article ID e24239, 2011.
- T. Stöcker and N. J. Shah, “Grundlagen der MR-Bildgebung,” in Funktionelle MRT in Psychiatrie und Neurologie, F. Schneider and G. R. Fink, Eds., pp. 61–78, Springer, Heidelberg, Germany, 2011.
- B. R. Brooks, K. Bushara, A. Khan et al., “Functional magnetic resonance imaging (fMRI) clinical studies in ALS—paradigms, problems and promises,” Amyotrophic Lateral Sclerosis and Other Motor Neuron Disorders, vol. 1, supplement 2, pp. S23–S32, 2000.
- S. Ogawa, R. S. Menon, D. W. Tank et al., “Functional brain mapping by blood oxygenation level-dependent contrast magnetic resonance imaging. A comparison of signal characteristics with a biophysical model,” Biophysical Journal, vol. 64, no. 3, pp. 803–812, 1993.
- C. Konrad, H. Henningsen, J. Bremer et al., “Pattern of cortical reorganization in amyotrophic lateral sclerosis: a functional magnetic resonance imaging study,” Experimental Brain Research, vol. 143, no. 1, pp. 51–56, 2002.
- C. Konrad, A. Jansen, H. Henningsen et al., “Subcortical reorganization in amyotrophic lateral sclerosis,” Experimental Brain Research, vol. 172, no. 3, pp. 361–369, 2006.
- M. A. Schoenfeld, C. Tempelmann, C. Gaul et al., “Functional motor compensation in amyotrophic lateral sclerosis,” Journal of Neurology, vol. 252, no. 8, pp. 944–952, 2005.
- B. R. Stanton, V. C. Williams, P. N. Leigh et al., “Altered cortical activation during a motor task in ALS: evidence for involvement of central pathways,” Journal of Neurology, vol. 254, no. 9, pp. 1260–1267, 2007.
- B. Mohammadi, K. Kollewe, A. Samii, R. Dengler, and T. F. Münte, “Functional neuroimaging at different disease stages reveals distinct phases of neuroplastic changes in amyotrophic lateral sclerosis,” Human Brain Mapping, vol. 32, no. 5, pp. 750–758, 2011.
- B. Mohammadi, K. Kollewe, A. Samii, K. Krampfl, R. Dengler, and T. F. Münte, “Decreased brain activation to tongue movements in amyotrophic lateral sclerosis with bulbar involvement but not Kennedy syndrome,” Journal of Neurology, vol. 256, no. 8, pp. 1263–1269, 2009.
- K. Kollewe, T. F. Münte, A. Samii, R. Dengler, S. Petri, and B. Mohammadi, “Patterns of cortical activity differ in ALS patients with limb and/or bulbar involvement depending on motor tasks,” Journal of Neurology, vol. 258, no. 5, pp. 804–810, 2011.
- B. Mohammadi, K. Kollewe, A. Samii, K. Krampfl, R. Dengler, and T. F. Münte, “Changes of resting state brain networks in amyotrophic lateral sclerosis,” Experimental Neurology, vol. 217, no. 1, pp. 147–153, 2009.
- C. F. Beckmann, M. DeLuca, J. T. Devlin, and S. M. Smith, “Investigations into resting-state connectivity using independent component analysis,” Philosophical Transactions of the Royal Society B, vol. 360, no. 1457, pp. 1001–1013, 2005.
- M. D. Greicius, G. Srivastava, A. L. Reiss, and V. Menon, “Default-mode network activity distinguishes Alzheimer's disease from healthy aging: evidence from functional MRI,” Proceedings of the National Academy of Sciences of the United States of America, vol. 101, no. 13, pp. 4637–4642, 2004.
- C. Sorg, V. Riedl, M. Mühlau et al., “Selective changes of resting-state networks in individuals at risk for Alzheimer's disease,” Proceedings of the National Academy of Sciences of the United States of America, vol. 104, no. 47, pp. 18760–18765, 2007.
- J. S. Damoiseaux, S. A. R. B. Rombouts, F. Barkhof et al., “Consistent resting-state networks across healthy subjects,” Proceedings of the National Academy of Sciences of the United States of America, vol. 103, no. 37, pp. 13848–13853, 2006.
- M. De Luca, C. F. Beckmann, N. De Stefano, P. M. Matthews, and S. M. Smith, “fMRI resting state networks define distinct modes of long-distance interactions in the human brain,” NeuroImage, vol. 29, no. 4, pp. 1359–1367, 2006.
- M. van den Heuvel, R. Mandl, and H. H. Pol, “Normalized cut group clustering of resting-state fMRI data,” PLoS ONE, vol. 3, no. 4, Article ID e2001, 2008.
- M. E. Raichle, A. M. MacLeod, A. Z. Snyder, W. J. Powers, D. A. Gusnard, and G. L. Shulman, “A default mode of brain function,” Proceedings of the National Academy of Sciences of the United States of America, vol. 98, no. 2, pp. 676–682, 2001.
- M. E. Raichle and A. Z. Snyder, “A default mode of brain function: a brief history of an evolving idea,” NeuroImage, vol. 37, no. 4, pp. 1083–1090, 2007.
- B. B. Biswal, J. van Kylen, and J. S. Hyde, “Simultaneous assessment of flow and BOLD signals in resting-state functional connectivity maps,” NMR in Biomedicine, vol. 10, no. 4-5, pp. 165–170, 1997.
- M. De Luca, S. Smith, N. De Stefano, A. Federico, and P. M. Matthews, “Blood oxygenation level dependent contrast resting state networks are relevant to functional activity in the neocortical sensorimotor system,” Experimental Brain Research, vol. 167, no. 4, pp. 587–594, 2005.
- M. J. Jafri, G. D. Pearlson, M. Stevens, and V. D. Calhoun, “A method for functional network connectivity among spatially independent resting-state components in schizophrenia,” NeuroImage, vol. 39, no. 4, pp. 1666–1681, 2008.
- K. Kollewe, T. F. Münte, A. Samii, R. Dengler, S. Petri, and B. Mohammadi, “Functional neuroimaging in amyotrophic lateral sclerosis: analysis of resting activity,” Klinische Neurophysiologie, vol. 40, no. 4, pp. 263–269, 2009.
- L. H. Ziegler, “Psychotic and emotional phenomena associated with amyotrophic lateral sclerosis,” Archives of Neurology and Psychiatry, vol. 24, pp. 930–936, 1930.
- B. Frank, J. Haas, H. J. Heinze, E. Stark, and T. F. Münte, “Relation of neuropsychological and magnetic resonance findings in amyotrophic lateral sclerosis: evidence for subgroups,” Clinical Neurology and Neurosurgery, vol. 99, no. 2, pp. 79–86, 1997.
- D. Irwin, C. F. Lippa, and J. M. Swearer, “Cognition and amyotrophic lateral sclerosis (ALS),” American Journal of Alzheimer's Disease and Other Dementias, vol. 22, no. 4, pp. 300–312, 2007.
- J. Lakerveld, B. Kotchoubey, and A. Kübler, “Cognitive function in patients with late stage amyotrophic lateral sclerosis,” Journal of Neurology, Neurosurgery and Psychiatry, vol. 79, no. 1, pp. 25–29, 2008.
- S. C. Woolley and J. S. Katz, “Cognitive and behavioral impairment in amyotrophic lateral sclerosis,” Physical Medicine and Rehabilitation Clinics of North America, vol. 19, no. 3, pp. 607–617, 2008.
- T. F. Münte, M. C. Tröger, I. Nusser et al., “Abnormalities of visual search behaviour in ALS patients detected with event-related brain potentials,” Amyotrophic Lateral Sclerosis and Other Motor Neuron Disorders, vol. 1, no. 1, pp. 21–27, 1999.
- K. S. Paulus, I. Magnano, M. R. Piras et al., “Visual and auditory event-related potentials in sporadic amyotrophic lateral sclerosis,” Clinical Neurophysiology, vol. 113, no. 6, pp. 853–861, 2002.
- P. Vieregge, B. Wauschkuhn, I. Heberlein, J. Hagenah, and R. Verleger, “Selective attention is impaired in amyotrophic lateral sclerosis—a study of event-related EEG potentials,” Cognitive Brain Research, vol. 8, no. 1, pp. 27–35, 1999.
- S. Abrahams, L. H. Goldstein, J. J. M. Kew et al., “Frontal lobe dysfunction in amyotrophic lateral sclerosis: a PET study,” Brain, vol. 119, no. 6, pp. 2105–2120, 1996.
- S. Abrahams, L. H. Goldstein, A. Simmons et al., “Word retrieval in amyotrophic lateral sclerosis: a functional magnetic resonance imaging study,” Brain, vol. 127, no. 7, pp. 1507–1517, 2004.
- J. J. M. Kew, L. H. Goldstein, P. N. Leigh et al., “The relationship between abnormalities of cognitive function and cerebral activation in amyotrophic lateral sclerosis: a neuropsychological and positron emission tomography study,” Brain, vol. 116, no. 6, pp. 1399–1423, 1993.
- F. Esposito, E. Formisano, E. Seifritz et al., “Spatial independent component analysis of functional MRI time-series: to what extent do results depend on the algorithm used?” Human Brain Mapping, vol. 16, no. 3, pp. 146–157, 2002.
- F. Esposito, T. Scarabino, A. Hyvarinen et al., “Independent component analysis of fMRI group studies by self-organizing clustering,” NeuroImage, vol. 25, no. 1, pp. 193–205, 2005.
- F. Esposito, A. Aragri, I. Pesaresi et al., “Independent component model of the default-mode brain function: combining individual-level and population-level analyses in resting-state fMRI,” Magnetic Resonance Imaging, vol. 26, no. 7, pp. 905–913, 2008.
- E. Formisano, F. Esposito, F. Di Salle, and R. Goebel, “Cortex-based independent component analysis of fMRI time series,” Magnetic Resonance Imaging, vol. 22, no. 10, pp. 1493–1504, 2004.
- R. Goebel, F. Esposito, and E. Formisano, “Analysis of functional image analysis contest (FIAC) data with brainvoyager QX: from single-subject to cortically aligned group general linear model analysis and self-organizing group independent component analysis,” Human Brain Mapping, vol. 27, no. 5, pp. 392–401, 2006.
- R. L. Buckner, J. R. Andrews-Hanna, and D. L. Schacter, “The brain's default network: anatomy, function, and relevance to disease,” Annals of the New York Academy of Sciences, vol. 1124, pp. 1–38, 2008.
- R. Cabeza, F. Dolcos, R. Graham, and L. Nyberg, “Similarities and differences in the neural correlates of episodic memory retrieval and working memory,” NeuroImage, vol. 16, no. 2, pp. 317–330, 2002.
- T. E. Goldberg, K. F. Berman, K. Fleming et al., “Uncoupling cognitive workload and prefrontal cortical physiology: a PET rCBF study,” NeuroImage, vol. 7, no. 4 I, pp. 296–303, 1998.
- C. Lomen-Hoerth, J. Murphy, S. Langmore, J. H. Kramer, R. K. Olney, and B. Miller, “Are amyotrophic lateral sclerosis patients cognitively normal?” Neurology, vol. 60, no. 7, pp. 1094–1097, 2003.
- J. M. Murphy, R. G. Henry, S. Langmore, J. H. Kramer, B. L. Miller, and C. Lomen-Hoerth, “Continuum of frontal lobe impairment in amyotrophic lateral sclerosis,” Archives of Neurology, vol. 64, no. 4, pp. 530–534, 2007.
- A. C. Ludolph, K. J. Langen, M. Regard et al., “Frontal lobe function in amyotrophic lateral sclerosis: a neuropsychologic and positron emission tomography study,” Acta Neurologica Scandinavica, vol. 85, no. 2, pp. 81–89, 1992.
- J. J. M. Kew, P. N. Leigh, E. D. Playford et al., “Cortical function in amyotrophic lateral sclerosis. A positron emission tomography study,” Brain, vol. 116, no. 3, pp. 655–680, 1993.
- J. J. M. Kew, D. J. Brooks, R. E. Passingham, J. C. Rothwell, R. S. J. Frackowiak, and P. N. Leigh, “Cortical function in progressive lower motor neuron disorders and amyotrophic lateral sclerosis: a comparative PET study,” Neurology, vol. 44, no. 6, pp. 1101–1110, 1994.
- G. Tedeschi, F. Trojsi, A. Tessitore et al., “Interaction between aging and neurodegeneration in amyotrophic lateral sclerosis,” Neurobiology of Aging, vol. 33, no. 5, pp. 886–898, 2012.
- G. Douaud, N. Filippini, S. Knight, K. Talbot, and M. R. Turner, “Integration of structural and functional magnetic resonance imaging in amyotrophic lateral sclerosis,” Brain, vol. 134, no. 12, pp. 3470–3479, 2011.
- E. Verstraete, M. P. van den Heuvel, J. H. Veldink et al., “Motor network degeneration in amyotrophic lateral sclerosis: a structural and functional connectivity study,” PLoS ONE, vol. 5, no. 10, Article ID e13664, 2010.
Copyright © 2012 Katja Kollewe et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.