Brain Struct Funct DOI 10.1007/s00429-013-0702-8

ORIGINAL ARTICLE

Motor cortex excitability and connectivity in chronic stroke: a multimodal model of functional reorganization Lukas J. Volz • Anna-Sophia Sarfeld • Svenja Diekhoff • Anne K. Rehme • Eva-Maria Pool • Simon B. Eickhoff • Gereon R. Fink • Christian Grefkes

Received: 13 September 2013 / Accepted: 26 December 2013 Ó Springer-Verlag Berlin Heidelberg 2014

Abstract Cerebral ischemia triggers a cascade of cellular processes, which induce neuroprotection, inflammation, apoptosis and regeneration. At the neural network level, lesions concomitantly induce cerebral plasticity. Yet, many stroke survivors are left with a permanent motor deficit, and only little is known about the neurobiological factors that determine functional outcome after stroke. Transcranial magnetic stimulation (TMS) and magnetic resonance imaging (MRI) are non-invasive approaches that allow insights into the functional (re-) organization of the cortical motor system. We here combined neuronavigated TMS, MRI and analyses of connectivity to investigate to which degree recovery of hand function depends on corticospinal tract (CST) damage and biomarkers of cerebral plasticity

Electronic supplementary material The online version of this article (doi:10.1007/s00429-013-0702-8) contains supplementary material, which is available to authorized users. L.J. Volz and A.-S. Sarfeld contributed equally to this work. L. J. Volz
A.-S. Sarfeld
S. Diekhoff
A. K. Rehme
E.-M. Pool
C. Grefkes (&) Neuromodulation and Neurorehabilitation, Max Planck Institute for Neurological Research, Gleueler Str. 50, 50931 Cologne, Germany e-mail: [email protected] L. J. Volz
A.-S. Sarfeld
G. R. Fink
C. Grefkes Department of Neurology, University of Cologne, Cologne, Germany S. B. Eickhoff
G. R. Fink
C. Grefkes Institute of Neuroscience and Medicine (INM-1, INM-3), Juelich Research Centre, Ju¨lich, Germany S. B. Eickhoff Institute for Clinical Neuroscience and Medical Psychology, Heinrich-Heine University, Du¨sseldorf, Germany

like cortical excitability and motor network effective connectivity. As expected, individual motor performance of 12 stroke patients with persistent motor deficits was found to depend upon the degree of CST damage but also motor cortex excitability and interhemispheric connectivity. In addition, the data revealed a strong correlation between reduced ipsilesional motor cortex excitability and reduced interhemispheric inhibition in severely impaired patients. Interindividual differences in ipsilesional motor cortex excitability were stronger related to the motor deficit than abnormal interhemispheric connectivity or CST damage. Multivariate linear regression analysis combining the three factors accounted for more than 80 % of the variance in functional impairment. The inter-relation of cortical excitability and reduced interhemispheric inhibition provides direct multi-modal evidence for the disinhibition theory of the contralesional hemisphere following stroke. Finally, our data reveal a key mechanism (i.e., the excitability-related reduction in interhemispheric inhibition) accounting for the rehabilitative potential of novel therapeutic approaches which aim at modulating cortical excitability in stroke patients. Keywords Stroke
Cortical excitability
Functional reorganization
TMS
DCM Abbreviations AH Affected hand AMT Active motor threshold ARAT Action research arm test CST Corticospinal tract DCM Dynamic causal modeling DTI Diffusion tensor imaging EMG Electromyography EPI Echo planar imaging

123

Brain Struct Funct

FDI FDR fMRI FWE FWHM GLM IHI JTT M1 MEP MNI MP-RAGE MRI mRS MSO NIHSS ROI rTMS SD SMA SPM tDCS TMS UH V1 vPMC

First dorsal interosseus muscle False discovery rate Functional magnetic resonance imaging Family-wise error Full width at half maximum General linear model Interhemispheric inhibition Jebsen–Taylor hand function test Primary motor cortex Motor evoked potential Montreal Neurological Institute Magnetization-prepared rapid acquisition gradient echo Magnetic resonance imaging Modified Rankin Scale Maximum stimulator output National Institutes of Health Stroke Scale Region of interest Repetitive transcranial magnetic stimulation Standard deviation Supplementary motor area Statistical parametric mapping Transcranial direct current stimulation Transcranial magnetic stimulation Unaffected hand Primary visual cortex Ventral premotor cortex

Introduction Stroke is a leading cause for disability in adults in Europe and the USA. Especially the impairment of upper limb function constitutes a major factor which interferes with functional independence and quality of life (Veerbeek et al. 2011). A number of studies have already identified neurobiological factors that are tightly linked to impaired motor functions after stroke: For example, extensive damage to the corticospinal tract (CST) has been found to be indicative for strong motor impairments (Stinear et al. 2007; Schulz et al. 2012). Likewise, reduced corticomotor excitability is often found in severely impaired patients (Macdonell et al. 1989; Escudero et al. 1998; Nardone and Tezzon 2002; Cicinelli et al. 2003). However, the variability of findings reported as well as the interindividual variability observed within the respective studies strongly suggests that a multimodal assessment of structure–function relationships might be more suited to explain strokeinduced behavioral deficits and recovery thereof. For example, Stinear et al. (2007, 2012) showed that noninvasive neuroimaging in combination with electrophysiological assessments to evaluate CST integrity can be used

123

to predict the individual potential of recovery in acute stroke patients. However, motor recovery also depends on changes at the cortical level. For example, longitudinal studies have shown that motor thresholds—which are rather stable over time in healthy subjects (Ngomo et al. 2012; Weiss et al. 2012)—undergo considerable changes during the process of motor recovery in patients (Catano et al. 1996; Turton et al. 1996; Byrnes et al. 1999; Thickbroom et al. 2002). Such changes are likely to reflect cerebral plasticity, i.e., the reorganisation of brain networks (Dancause and Nudo 2011; Pekna et al. 2012). Likewise, longitudinal functional neuroimaging studies found dynamic changes in motor network activity closely related to recovery. Here, movements of the stroke-affected hand are associated with a successive increase of functional MRI (fMRI) activity in both hemispheres in the first few weeks after stroke, which then return to levels observed in healthy controls, particularly in patients making full motor recovery (Calautti et al. 2001; Ward et al. 2003; Gerloff et al. 2006; Grefkes et al. 2008; Rehme et al. 2012). These changes in fMRI activity over time are mirrored by changes in effective connectivity between motor areas, with reduced neural coupling between premotor areas and ipsilesional primary motor cortex (M1), as well as reduced interhemispheric coupling between ipsi- and contralesional M1 in the acute phase after stroke, which also normalize concurrent to functional recovery (Grefkes et al. 2008; Rehme et al. 2011). To date, however, our understanding of how these stroke-outcome associated factors interact is limited as only few studies have thus far pursued a multimodal approach to explain stroke-induced symptoms (Stinear et al. 2007, 2012). For example, connectivity analyses of cortico-cortical interactions remote from the primary lesion site might provide important additional information on the determinants of motor recovery beyond the amount of structural CST damage (Grefkes and Fink 2011). An integrative perspective, accounting for both the impact of structural damage as well as compensatory cortical changes (i.e., altered motor network interaction, M1 excitability), seems highly relevant when exploring novel non-invasive brain stimulation techniques like repetitive transcranial magnetic stimulation (rTMS) or transcranial direct current stimulation (tDCS) that aim at enhancing recovery of function (Hummel and Cohen 2006; Kandel et al. 2012). We, therefore, conducted a cross-sectional, multi-modal study using neuronavigated TMS, structural and functional MRI, as well as analyses of effective connectivity to test whether, and if so to which degree, variance in individual motor impairment following stroke depends upon changes in cortical excitability and effective connectivity in addition to structural damage to the CST. Although cortical reorganization likely relates to the amount of CST

Brain Struct Funct

damage—with more severe tissue loss entailing more extensive compensation on the cortical level (Ward et al. 2006)—we hypothesized that changes at the cortical level are particularly indicative of the individual functional outcome over and above the structural damage. Therefore, we combined estimates of excitability, cortical connectivity, and CST damage within different regression models to test which factors explain best motor performance of chronic stroke patients. We furthermore hypothesized that changes in motor-cortical excitability are paralleled by abnormal interactions among cortical motor areas within and across the hemispheres (Talelli et al. 2006; Rehme et al. 2011)—possibly furthering our insights into the mechanisms underlying cortical reorganization in stroke.

Materials and methods Subjects Twelve patients (mean age 63 years, standard deviation (SD) 10 years, 1 left-handed subject) with mild to moderate unilateral hand motor deficits due to a first-ever ischemic stroke and healthy subjects participated after providing informed written consent. Stroke lesions were located within the middle cerebral artery territory of the right (9 patients) or left hemisphere (3 patients, see Table 1 for details). Patients were included based on the following criteria: (1) stroke in the chronic stage (i.e., [1 year postinsult), (2) unilateral motor deficit, (3) absence of aphasia, neglect, apraxia, or epilepsy, (4) no mirror movements, and (5) ability to perform the experimental protocol with both hands. The clinical deficit was rated using the National Institutes of Health Stroke Scale (NIHSS, Brott et al. 1989) and the modified Rankin Scale (mRS, van Swieten et al. 1988). Eleven age-matched subjects (mean age 60 years, SD 9 years, one left-handed subject) with no history of neurological, psychiatric or relevant orthopedic diseases served as controls. The study was approved by the local ethics committee and performed in accordance with the Declaration of Helsinki. Motor tests The following motor tests were used to quantify the motor impairment of the patients: (1) maximum fist-closure frequency, (2) Action research arm test (ARAT), and (3) Jebsen–Taylor hand function test (JTT). All tests were performed with both hands. The maximum fist-closure frequency is a simple but sensitive index for hand motor impairment, and was assessed by averaging three 5 s trials. The ARAT provides a more global measure of upper limb function along the four dimensions (a) grasp, (b) grip,

(c) pinch, and (d) gross movements (Lyle 1981; Yozbatiran et al. 2008). The JTT is a speed-based test that probes upper limb function in simulated daily living situations like eating or drinking (Yozbatiran et al. 2008). Stimulation with neuronavigated TMS and EMG recordings Stereotaxic frameless neuronavigated TMS was performed using the eXimia NBS system version 3.2 (Nexstim, Helsinki, Finland). The head of the subject was co-registered with the individual high-resolution anatomical MR image using anatomical landmarks. After anatomical co-registration, a figure-of-eight coil was placed tangentially to the scalp in an orientation inducing a posterior–anterior current perpendicular to the main course of the central sulcus. Electromyographic (EMG) recordings were obtained from the right and left first dorsal interosseus muscle (FDI) using Ag/AgCl surface electrodes (Tyco Healthcare, Neustadt, Germany) placed in a belly-to-tendon montage. The EMG signal was amplified, filtered (0.5 Hz high-pass and 30–300 Hz band-pass) and digitized using a PowerLab 26 T and LabChart software package version 6.0 (ADInstruments Ltd, Dunedin, New Zealand). The ‘‘motor hotspot’’, i.e., position eliciting the highest motor evoked potential (MEP) in the FDI with lowest intensity (Rothwell et al. 1999), was identified near the ‘‘hand knob’’, i.e., the anatomical landmark of the motor hand area in both hemispheres (Yousry et al. 1997). These individual coil positioning parameters were logged into the neuronavigation software as reference for all subsequent stimulations. This approach ensured that the distance between the stimulation hot-spot and the electric field maximum induced by the TMS coil was below 2 mm. Active motor threshold We assessed excitability of the corticospinal system using the ‘‘active motor threshold’’ (AMT) of the motor hotspot, which predominantly reflects the cortical motor output compared to the ‘‘resting motor threshold’’ which is also influenced by spinal and other influences (Day et al. 1989; Devanne et al. 1997; Paulus et al. 2008). The AMT was defined as the lowest stimulator intensity needed to evoke MEPs of 200 lV in five out of ten consecutive trials during mild tonic contraction of the FDI (at 10–20 % maximum force of the FDI; Sarfeld et al. 2012; Di Lazzaro et al. 1998). fMRI motor paradigm We employed an fMRI block design in which subjects were asked to perform hand movements with their affected

123

123

56

65

70

68

65

73

75

42

67

60

52

73

62.8

10.3

1

2

3

4

5

6

7

8

9

10

11

12

Mean

SD

F

F

M

M

F

M

M

M

M

M

M

M

Sex

R

R

R

R

R

R

L

R

R

R

R

R

Handedness

Capsula interna and basal ganglia Pons

La

Capsula interna

Paraventricular WM lesions underneath central sulcus

R

R

L

a

Inferior frontal cortex

Thalamus

R

Thalamus

R

Basal ganglia

Corona radiata, basal ganglia, insular cortex

Thalamus

WM lesions underneath central gyrus

WM lesions in superior, middle frontal gyrus

Lesion location

La

R

R

R

R

R

Lesional hemisphere

62.0

55.5

50

12

16

253

18

43

35

22

32

100

28

33

Lesion age (m)

0.8

1.5

1

1

1

1

2

1

1

2

3

3

1

1

mRS

1.9

3.2

4

1

2

3

3

2

4

3

5

7

1

1

NIHSS

0.57

0.97

1.13

0.79

0.79

0.91

0.33

0.85

1.00

0.78

0.53

0.68

0.57

0.97

Relative max. fist-closure frequency

12.1

45.4

54

57

54

56

32

57

41

50

32

24

46

57

0.0

57.0

57

57

57

57

57

57

57

57

57

57

57

57

51.0

77.7

41.1

38.7

46.5

34.0

144.8

27.7

69.4

68.5

165.2

158.2

64.8

30.7

Aff. hand

Aff. hand

Unaff. hand

JTT (s)

ARAT

6.8

36.1

33.1

28.6

43.0

32.9

33.0

26.7

37.6

47.3

39.5

47.2

36.4

27.5

Unaff. hand

a

Subject’s images were flipped, see ‘‘Materials and methods’’ for details

mRS modified Rankin Scale, NIHSS National Institutes of Health Stroke Scale, ARAT Action Research Arm Test, JTT Jebsen–Taylor task performance, SD standard deviation, M male, F female, R right, L left, WM white matter, m months, s seconds

Age

Patient

Table 1 Demographical, clinical, and behavioral data of stroke patients

Brain Struct Funct

Brain Struct Funct

(AH) or unaffected hand (UH) paced by a rhythmic visual cue. The task was to press MR-compatible response grips positioned between proximal parts of the thumb and index finger according to the rhythm of the blinking visual cue. This set-up specifically facilitates movements of the FDI, which was used as reference muscle for TMS. In addition, the task performance was recorded by an MR-compatible camera at the foot-end of the scanner bed. The software ‘‘Presentation’’ (Version 9.9, Neurobehavioral Systems, Inc., CA, USA) was used for visual stimulus presentation and recording of pressing the response grips during a given experimental condition. After a brief instruction text, indicating which hand to use in the upcoming block of movements, a circle blinking in red color at a frequency of 0.8 Hz indicated the subjects to press the response grips in the same rhythm. Each motor task block (lasting 15 s) was followed by 14 s of rest. Similar motor paradigms have been used in the past to evoke BOLD activity in the motor system of stroke patients on a single-subject level which is an important prerequisite for regions of interest (ROIs)-based connectivity approaches as used in the present study (Grefkes et al. 2008; Rehme et al. 2011; Wang et al. 2011). MRI data acquisition Whole-brain functional MR images were acquired using a 3-Tesla Siemens MAGNETOM TIM Trio scanner (Siemens AG, Germany). We used a gradient echo planar imaging (EPI) sequence with the following imaging parameters: TR 2,070 ms, TE 30 ms, FoV 200 mm, 31 axial slices, slice thickness 3.1 mm, in-plane resolution 3.1 9 3.1 mm2, flip angle 90° and distance factor 20 %, 537 volumes including 3 dummy images. The slices covered the whole brain extending from fronto-parietal cortex to lower parts of the cerebellum. For all subjects, highresolution anatomical images were acquired using a 3D magnetization-prepared, rapid acquisition gradient echo (MP-RAGE) sequence with the following parameters: TR 2,000 ms, TE 3.25 ms, FOV 256 mm, 176 sagittal slices, slice thickness 1.0 mm, in-plane resolution 1.0 9 1.0 mm2, flip angle 9°. In addition, high-resolution anatomical T2 images were acquired, which were used to delineate lesioned brain tissue (TR 5,500 ms, FOV 220 mm, 48 axial slices, slice thickness 2 mm, distance factor 30 %, in-plane resolution 0.7 9 0.7 mm2, flip angle 90°). fMRI analyses We used Statistical Parametric Mapping (SPM8; Wellcome Department of Imaging Neuroscience, London, UK, http:// www.fil.ion.ucl.ac.uk) as implemented in Matlab (The Mathworks Inc.; MA, USA) for image preprocessing,

statistical analysis and dynamic causal modeling (DCM). Images from patients with left-sided lesions (n = 3 out of 12) were flipped along the midsagittal plane, so that the affected hemisphere corresponded to the right hemisphere in all patients. To account for systematic confounds resulting from hemispheric differences, the MR images of three controls matched for sex, handedness and age were flipped accordingly. The fMRI time series were corrected for movement artifacts using the Artifact Repair toolbox (ArtRepair, http:// cibsr.stanford.edu/tools/ArtRepair/ArtRepair.htm) as implemented in SPM8. The time series were co-registered with the T1-weighted image of the respective subjects. All images were spatially normalized to the standard template of the Montreal Neurological Institute (MNI, Canada) using the unified segmentation approach (Ashburner and Friston 2005). An isotropic Gaussian kernel with 4 mm full width at half maximum (FWHM) was applied as spatial smoothing filter to preserve spatial specificity of the fMRI data. For single-subject analyses, the experimental conditions were modeled in the framework of a General linear model (GLM) using boxcar stimulus functions convolved with a canonical hemodynamic response function as implemented in SPM8 and its first-order temporal derivative. For detecting significant activity on the group level, contrast images of the respective conditions (affected/unaffected hand movements) were entered into a full-factorial GLM. A threshold of P \ 0.05 (cluster-level family-wise error (FWE) corrected; cluster-forming threshold P \ 0.001 uncorrected at the voxel level) was employed to detect significant neural activity. The coordinates of the local activation maxima at the group level served as starting points to identify the motor ROIs at the single-subject level for the ensuing connectivity analysis. Dynamic causal modeling We used DCM (Friston et al. 2003) to estimate effective connectivity among key motor areas activated by the fMRI motor task (Grefkes et al. 2008; Rehme et al. 2011; Wang et al. 2011; Eickhoff and Grefkes 2011). We extracted the first eigenvariate of the BOLD time series in six motor ROIs: supplementary motor area (SMA), ventral premotor cortex (vPMC), and M1 at subject specific coordinates within spheres of 4 mm radius around individual activation maxima in individual, normalized SPMs (Table 2). In addition, as hand movements were triggered by a blinking visual cue, we modeled bilateral primary visual cortex (V1) activity as sensory input region driving (pre)motor activity (for details see, e.g., Grefkes et al. 2008). While all ROIs represent key nodes of the cortical motor network, other areas may also contribute to task performance. Importantly, the relay of neural information by regions that were not

123

123

-18

6

Mean

SD

-16

-26

-20

-24

-24

-22

-16

-16

-26

-20

-28 -22

4

1

2

3

4

5

6

7

8

9

10

11 Mean

SD

Control

-18

12

-94

-22

-22 -18

-12

8

9

-20

7

10 11

-102

-24

6

-92

4

-92 -96

-96

-98

-102

-96

-90

-96

-96

-92

-90

-102

3

-98

-92

-102 -98

-96

-98

-96

-30

-96

-10

-22

3

-100

-100

4

-16

2

5

-14

1

Patient

V1 L

5

-2 -2

-2

2

6

2

2

-4

4

-8

-12

-6

7

0

0

4 4

-2

-2

10

2

-16

2

-16

-8

2

5

26 24

28

32

18

24

30

22

28

16

20

20

8

21

30

28 10

20

22

28

34

10

34

14

14

16

V1 R

3

-94 -94

-92

-92

-92

-94

-90

-92

-94

-94

-100

-96

5

-91

-88

-94 -96

-96

-84

-92

-90

-90

-82

-84

-96

-98

3

2 1

6

4

0

2

0

-2

-2

-2

-2

2

7

-6

-8

0 2

6

-8

0

2

-14

0

-14

-12

-8

2

-6 -7

-6

-6

-8

-10

-8

-6

-6

-8

-6

-4

3

-6

-4

-4 -8

-4

-8

-12

-10

-4

-6

-6

-4

-4

SMA L

4

-16 -10

-16

-6

-12

-6

-12

-10

-10

-12

-4

-10

6

-8

-10

-12 -8

-18

-12

-2

-4

-4

-12

18

2

-4

Table 2 Individual fMRI activation maxima used as ROIs for DCM

4

68 67

64

66

64

74

66

62

64

68

74

62

6

62

60

64 64

70

58

58

66

62

56

50

56

74

2

4 6

6

4

6

6

12

4

6

6

6

8

2

5

8

6 4

6

4

4

4

6

4

10

4

4

4

-2 -8

-12

-2

-6

-8

-12

-8

-12

-14

-4

-8

4

-7

-10

-10 -6

-16

-8

-10

0

-14

-2

-6

-2

-6

SMA R

4

68 67

62

72

72

66

66

68

60

70

62

70

5

64

62

58 66

58

60

62

68

62

74

68

60

66

4

-62 -55

-54

-56

-58

-54

-58

-52

-52

-62

-48

-52

5

-54

-58

-60 -44

-52

-56

-48

-62

-52

-54

-58

-52

-56

6

-2 2

-2

6

-8

-4

6

10

0

10

10

0

7

0

14

6 8

-12

0

-2

8

-2

4

-10

-8

0

vPMC L

5

38 41

44

42

50

48

40

44

36

36

40

32

6

38

30

32 34

46

38

36

42

34

42

38

50

42

3

58 56

54

54

64

56

56

54

56

56

56

56

5

56

54

58 60

56

40

58

58

56

62

58

54

60

5

6 5

0

14

6

-2

0

6

0

8

2

14

7

2

-10

6 2

2

20

4

2

0

6

-6

-2

0

vPMC R

4

46 43

46

42

34

48

44

44

46

40

42

40

6

38

44

34 36

40

36

44

40

34

24

44

46

42

6

-44 -45

-52

-54

-42

-46

-36

-42

-40

-54

-42

-42

5

-39

-34

-42 -36

-36

-44

-42

-46

-42

-36

-44

-38

-48

M1 L

7

-28 -27

-36

-30

-36

-10

-26

-26

-22

-26

-28

-34

5

-30

-34

-34 -32

-34

-28

-32

-22

-32

-20

-30

-22

-22

9

54 60

56

36

72

60

56

70

60

64

62

66

3

62

70

64 74

58

66

58

60

56

66

58

60

58

7

40 44

38

42

38

46

36

58

50

40

52

42

3

37

40

40 40

40

36

36

44

36

32

36

34

36

M1 R

6

-28 -23

-28

-16

-26

-20

-30

-18

-10

-28

-16

-28

6

-27

-30

-34 -26

-36

-18

-26

-20

-30

-22

-28

-30

-24

4

54 58

56

56

64

62

50

54

56

60

60

58

8

63

58

64 70

50

68

62

70

58

72

48

70

58

Brain Struct Funct

Brain Struct Funct

explicitly modeled (e.g., frontoparietal areas) is implicitly represented by the coupling parameters between two regions (Friston et al. 2003; Stephan et al. 2009). All ROI coordinates were defined for baseline contrasts at a threshold of P \ 0.001 uncorrected, according to anatomical constraints as extensively described elsewhere (Rehme et al. 2011). Endogenous connections between the ROIs were defined based on animal studies as described in earlier studies (Grefkes et al. 2008; Rehme et al. 2011). As the condition-specific modulation of interregional coupling may not necessarily affect all possible anatomical connections, a total of 37 alternative connectivity models representing biologically plausible hypotheses on interregional coupling were constructed. The models systematically varied in complexity and number of connections ranging from sparsely to fully connected models (Suppl. Fig. 1A, B). After estimation of all 37 models, we used Bayesian model selection (Penny et al. 2004) to identify the model yielding the highest evidence given the data using a random effect approach (Stephan et al. 2009). According to Bayesian model selection, the fully connected model (model #1 out of 37 tested models) was the most likely generative model in both patients and controls (Suppl. Fig. 1A, B). The estimated coupling parameters of the ‘‘winning’’ model in patients and controls were tested for statistical significance by means of one-sample t tests (P \ 0.05, false discovery rate (FDR) corrected for multiple comparisons; Benjamini and Hochberg 1995). T2 lesion maps and CST damage In each patient, the lesion caused by ischemic damage was delineated on T2-weighted brain images. The degree of CST damage was assessed based on probabilistic myeloarchitectonic maps (Burgel et al. 1999) as implemented in the SPM Anatomy Toolbox (Eickhoff et al. 2005). To this end, individual lesion maps were created based on MNI-normalized T2 images and subsequently superimposed upon the probability map data. CST damage was defined as intersection volumes of individual lesions relative to the total CST volume. To investigate correlations between CST damage and cortical excitability as well as motor impairment, Pearson’s correlation coefficients were computed between percent CST damage and (1) the AMT of the ipsilesional hemisphere, and (2) individual motor performance (maximum fist-closure frequency, ARAT and JTT scores).

Results In patients, behavioral performance of the affected hand was significantly reduced in all motor tests when compared to the unaffected hand (ARAT: P = 0.011, JTT: P = 0.020, fist-

Fig. 1 Cortical excitability as active motor threshold (AMT) of the ipsilesional and contralesional hemisphere in patients and controls (as % of maximal stimulator output ± SD). Note the significant difference between the contralesional hemisphere of patients and controls (P \ 0.05, two-sided t test) and the considerably larger amount of variance of the AMT in the ipsilesional hemisphere of the patient group

closure frequency: P = 0.011; two-sided t test; Table 1) or controls (ARAT: P = 0.011, JTT: P = 0.002, fist-closure frequency: P = 0.005; two-sided t test; Table 1). The amount of CST damage (estimated by computing the intersection volume of the T2 lesion and the maximum probability map of the CST, see ‘‘Materials and methods’’) significantly correlated with poorer motor performance as assessed via maximum fist-closure frequency (r = -0.712, P = 0.009) and JTT-score (r = 0.628, P = 0.029), but not ARAT-score (Spearman-rho = -0.326, P = 0.301). Cortical excitability In patients, we found a trend for an increased ipsilesional AMT (i.e., less cortical excitability) compared to the contralesional hemisphere (Fig. 1; P = 0.078; two-sided t test), while AMTs were not different between hemispheres in controls (P = 0.175). Ipsilesional AMTs were not significantly higher when compared to healthy controls (P = 0.889). This finding may, at least in part, be related to the fact that the variance in AMT was considerably larger in the ipsilesional motor cortex compared to controls (Fig. 1). Therefore, our patient sample reflected a broader spectrum of excitability compared to age-matched healthy controls (AMT range 23–60 % maximum stimulator output (MSO) in the patient group; 25–39 % MSO in the control group). In addition, the AMT of the contralesional hemisphere was significantly smaller compared to the control group, suggesting a relative disinhibition of contralesional M1 (AMT range 20–32 % MSO; P = 0.029). Healthy subjects did not show any significant correlation between AMT and behavioral scores (P [ 0.2 for all comparisons). In contrast, patients showed negative

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Brain Struct Funct Fig. 2 a Neural activity during movements of the affected hand in patients and the corresponding hand in controls (P \ 0.05, FWE corrected on the cluster level, cluster forming threshold: P \ 0.001 uncorrected). b Activity in M1 of the contralesional hemisphere was significantly higher in patients compared to controls illustrated by the parameter estimates extracted from contralesional M1 (MNI coordinates -36 -24 68). A significant negative correlation was evident between motor performance of the affected hand and BOLD signal in the contralesional M1, with higher levels of neural activation observed in patients featuring stronger motor impairment

correlations between the AMT of the ipsilesional hemisphere and fist-closure frequency (affected hand relative to unaffected hand; r = -0.854, P \ 0.001). Likewise, the JTT (r = 0.658, P = 0.020) and the ARAT score (Spearman-rho = -0.609, P = 0.036) of the affected hand also significantly correlated with the AMT of the ipsilesional hemisphere. Furthermore, a significant correlation was found between AMT of the ipsilesional hemisphere and estimated CST damage (r = 0.732, P = 0.007), with greater damage to the CST found in patients featuring higher AMTs (i.e., reduced cortical excitability). No significant correlation was evident between any motor parameter and the AMT of the contralesional hemisphere (P [ 0.2 for all comparisons). In summary, the AMT of the ipsilesional hemisphere in patients featured a clear and patient-specific relationship with individual hand motor deficit, with reduced cortical excitability in patients featuring strong motor impairment.

healthy subjects. In contrast, movements of the strokeaffected hand were associated with significant BOLD signal increases in both hemispheres, especially within M1 of the contralesional hemisphere (Fig. 2a). To investigate whether this significant increase in BOLD activation within M1 of the contralesional hemisphere was related to motor impairment, Pearson’s correlation coefficients were calculated between the first eigenvariate of the BOLD signal time course within a 4 mm diameter sphere centered at the M1 peak voxel of the group analysis and fist-closure index, JTT and ARAT. A significant negative correlation was evident for the fistclosure index (r = -0.722, P = 0.008), with stronger hand function deficits in patients featuring higher levels of BOLD activation during movement of the affected hand (Fig. 2b). In contrast, the level of BOLD signal in contralesional M1 did not correlated with cortical excitability of either hemisphere.

fMRI group results

Dynamic causal modeling

In patients, movements of the unaffected hand revealed similar BOLD activity as observed for hand movements in

Endogenous connectivity parameters—reflecting the constant part of interregional coupling throughout the entire

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Fig. 3 Effective connectivity during movements of the affected hand estimated with DCM for controls and patients (a). Coupling parameters [in 1/s (Hz)] indicate connection strength, which is coded in color. Positive values (green) refer to promotion of neural activity. Negative values (red) indicate inhibition of neural activity (P \ 0.05,

FDR corrected, numbers in brackets: statistical trends: P \ 0.055). b Significant differences in coupling parameters between controls and stroke patients (changes in coupling strength next to arrows, P \ 0.05). IL ipsilesional hemisphere, CL contralesional hemisphere

experiment (DCM-A; Suppl. Fig. 2)—did not correlate with AMT, CST damage or any of the behavioral scores. We, therefore, focused all subsequent analyses on the hand-movement-associated changes in effective connectivity as represented in the DCM-B matrix. Hand-movement associated modulation of connectivity In healthy subjects, left or right hand movements were associated with increased positive coupling of SMA and PMC with M1, contralateral to the moving hand. By contrast, almost all connections towards the opposite (ipsilateral) M1 showed negative coupling parameters (Fig. 3a, P \ 0.05, FDR corrected). Significant differences between patients and controls were exclusively observed for coupling estimates associated with movements of the affected hand: Here, the activity of contralesional M1 was significantly less inhibited by ipsilesional M1, and SMA, as well as contralesional premotor areas (Fig. 3b, P \ 0.05). Thus, the DCM analysis suggested a relative disinhibition of the contralesional M1 during movement of the affected hand compared to healthy controls. When correlating DCM coupling parameters with motor performance (relative fist-closure frequency), we observed significant positive correlations for the coupling between ipsilesional SMA and contralesional M1 as well as between ipsilesional M1 and contralesional M1 (P \ 0.05, FDR corrected). This means that patients with stronger functional impairments featured less interhemispheric inhibition of the unaffected M1. This finding is consistent with

Fig. 4 Significant Pearson correlations between the AMT and DCM coupling parameters were evident for excitatory input from SMA onto M1 in healthy controls (a). This connection did not correlate with motor function of the respective contralateral hand. By contrast, in patients, the interhemispheric connections from ipsilesional–contralesional M1 and ipsilesional SMA-contralesional M1 correlated to both: the AMT of the ipsilesional hemisphere and motor performance of the affected hand (b). Note that both connections were significantly reduced in patients compared to controls (see Fig. 3b; estimated for movements of the affected/left hand; FDR corrected; asterisk: significant correlation of the given connection with the relative maximum fist-closure index of the affected/unaffected hand). IL ipsilesional hemisphere, CL contralesional hemisphere

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the BOLD data showing that activity in contralesional M1 correlated with reduced fist-closure frequency (Fig. 2). Hence, the DCM results suggest that ‘‘over-activity’’ of the contralesional hemisphere resulted from reduced transcallosal inhibition exerted by motor areas of the affected hemisphere and that this neural signature is associated with reduced motor performance. DCM coupling and cortical excitability In healthy controls, we found a significant negative correlation between AMT and SMA-M1 coupling of the corresponding hemisphere during movements of the contralateral hand (r = -0.913; Fig. 4a). Thus, the stronger the activity-dependent excitatory input from SMA onto M1, the less intensity was needed (outside the scanner) to evoke an MEP from the activated cortical motor system. In contrast, in stroke patients, no such relationship was observed. Instead, there was a positive correlation between the AMT of the ipsilesional hemisphere and the movementdependent coupling from ipsilesional SMA onto contralesional M1 (r = 0.814) as well as the influence exerted by ipsilesional M1 onto contralesional M1 (r = 0.746; P \ 0.05, FDR corrected; Fig. 4b). These correlations remained significant when computing a partial correlation with the amount of CST damage as a covariate (ipsilesional M1–contralesional M1 r = 0.608, P = 0.047; ipsilesional SMA–contralesional M1 r = 0.670, P = 0.024). Hence, higher intensities were needed to evoke MEPs in the ipsilesional hemisphere featuring a relative disinhibition of the contralesional M1 (Fig. 3), independent of the estimated damage to corticospinal fibers. Interestingly, these connections which correlated with the AMT were also among those connections that were significantly weaker when compared to healthy controls (Fig. 3b), and which correlated with reduced motor performance (Fig. 4b). Again, the correlation between these two inhibitory connections targeting the contralesional M1 (Fig. 3a) and motor performance remained significant after controlling for the amount of CST damage in a partial correlation. In addition, at an uncorrected statistical threshold, also the connection from ipsilesional vPMC targeting contralesional M1 correlated with motor performance scores. Importantly, coupling parameters estimated for movements of the unaffected hand (Suppl. Fig. 2) did not correlate with the AMT of the contralesional hemisphere (P [ 0.958), thus highlighting the specificity of the findings for the ipsilesional hemisphere. Motor function in chronic stroke We finally addressed the question to which extent a combination of all these factors might explain the variance

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observed in individual motor performance. We, therefore, entered the variables ‘‘percent CST damage’’, ‘‘ipsilesional AMT’’, and ‘‘DCM coupling ipsilesional M1-contralesional M1’’ into a linear regression model (as implemented in IBM SPSS Statistics, version 21) to explain the variance in hand motor performance of the paretic hand. We used the maximum fist-closure frequency of the affected hand relative to the unaffected hand as a marker of motor performance since this kind of task was most specific to the variables of interest (fMRI motor task and AMT of the FDI). When considering each variable alone, the highest amount of variance was explained by the AMT (73 % explained variance, r = 0.85, F1,10 = 26.8, P \ 0.001), followed by DCM–M1 coupling (64 % explained variance, r = 0.80, F1,10 = 18.1, P = 0.002) and CST damage (51 %, r = 0.71, F1,10 = 10.3, P = 0.009). However, combining all these variables in one model explained 81 % of motor performance of the paretic hand (r = 0.90, F3,8 = 11.7, P = 0.003). Therefore, this model suggests that persistent impairments of hand motor function after stroke are not only reflected by the amount of CST damage. Rather, excitability changes in ipsilesional M1 as well as interhemispheric connectivity changes beyond the structural damage seem to contribute to motor function in chronic stroke.

Discussion Our results in chronic stroke patients suggest a relationship between motor impairment and the amount of structural CST damage, as well as surrogate markers of reduced cortical excitability and interhemispheric connectivity. The main finding of our study is that the AMT of the ipsilesional hemisphere explained the largest amount of variance in individual motor impairment in chronic stroke patients (73 %), followed by reduced interhemispheric connectivity (64 %) and the amount of structural CST damage (51 %). In addition, the combination of all three factors accounts for more than 80 % of variance in motor impairment. Thus, partial correlations and linear regression models reveal that persistent strokeinduced motor impairment is influenced not only by the lesion itself but also by aspects of cortical reorganization, confirming our initial hypothesis. Furthermore, our data suggest that reduced ipsilesional motor-cortical excitability is associated with both disinhibition of contralesional M1 and the degree of structural damage to the CST. These findings not only provide evidence for the disinhibition theory of the contralesional hemisphere after stroke, but also suggest that AMT may serve as a biomarker, indicative of both structural damage and cortical reorganization patterns.

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Structural damage in stroke Previous studies suggest that in stroke the integrity of the CST is indicative for recovery of motor function, with greater structural damage resulting in more severe motor impairment (Stinear et al. 2007, 2012; Wang et al. 2012). We found a relationship between the amount of CST damage and motor impairment, consistent with prior studies: in our study, CST damage explained 51 % of the variance in motor performance of the stroke-affected hand. In addition to motor performance, TMS parameters of corticospinal transmission have been linked to structural markers of CST damage. For example, Liepert and colleagues (2005) showed that patients with lesions at the level of the internal capsule or pons often feature increases in motor thresholds while patients with basal ganglia lesion did not. These data match our own finding that an increase of the AMT correlates with the degree of CST damage, and might partly reflect the fact that lesions to descending CST fibers may cause dispersion in conduction of activity induced by TMS over M1. The resulting loss of synchronicity and an ensuing decrease in MEP amplitudes would cause a subsequent increase in motor thresholds (Talelli et al. 2006). Furthermore, the functional deafferentation of the ipsilesional cortex due to CST damage was suggested to activate intracortical inhibitory circuits, which could also lead to reduced motor-cortical excitability (von Giesen et al. 1994; Classen et al. 1997; Liepert 2005; Liepert et al. 2005). This might represent an additional aspect involved in M1 excitability changes following stroke. Regarding motor performance, inter-patient differences in AMT explained more variance than CST damage. Therefore, AMT seems to reflect factors that contribute to recovered hand function following stroke over and above CST damage. Cortical changes following stroke A significant relationship between the AMT and the degree of motor impairment in patients having recovered from stroke has been described previously (Byrnes et al. 1999; Thickbroom et al. 2002). Importantly, in the first weeks and months after stroke, excitability changes of the motor cortex are dynamic as a function of time and recovery (Byrnes et al. 1999; Thickbroom et al. 2002). Therefore, in the stable (i.e., chronic) phase post-stroke, TMS parameters of cortical excitability not only reflect direct effects of lesions onto the integrity of the corticospinal system but also effects resulting from cerebral reorganization. A complementary way to assess cerebral reorganization following stroke is enabled by functional neuroimaging, which has been used to demonstrate dynamic changes in motor network activity and connectivity alongside motor recovery (Calautti et al. 2001; Ward et al. 2003; Gerloff

et al. 2006; Grefkes et al. 2008; Rehme et al. 2012). A consistent finding of these studies is that fMRI activity during movements of the affected hand is increased in cortical motor areas of both hemispheres, a pattern which persists in patients with poor recovery of function (Ward et al. 2003; Rehme et al. 2011). This latter effect can also be observed in our patient sample, especially with respect to BOLD activity in contralesional M1 while the paretic hand is moved (Fig. 2b). Interhemispheric disinhibition Interhemispheric connectivity has also been demonstrated to be altered after stroke. For example, a longitudinal DCM study showed that inhibitory M1–M1 coupling is significantly reduced in acute stroke but returns to levels comparable to those observed in healthy subjects when patients recover (Rehme et al. 2011). Our data which showed reduced interhemispheric inhibition of the contralesional M1 in patients with persistent motor impairment compared to controls are consistent with these previous observations (Fig. 3b). Independent evidence stems from TMS studies using paired-pulse protocols, which also reported reduced interhemispheric inhibition in chronic stroke patients (Boroojerdi et al. 1996; Shimizu et al. 2002). Furthermore, an increase in cortical excitability of contralesional M1 has also been reported by other groups (Cicinelli et al. 1997; Traversa et al. 1998), similar to the findings of the present study (Fig. 1). In addition, we found a patient-specific, i.e., individual relationship between cortico-cortical connectivity, AMT and motor performance: the inhibitory influences exerted by ipsilesional SMA and ipsilesional M1 onto contralesional M1 did not only correlate with motor function but also with the AMT of the ipsilesional hemisphere (Fig. 4). In principle, two mechanisms may account for interhemispheric disturbances after stroke. First, lesions might not only affect the CST but also fibers that connect the two hemispheres, resulting in a degeneration of callosal tracts. Support for this hypothesis comes from recent diffusion tensor imaging (DTI) studies which showed that in patients with persistent stroke-induced motor deficits parts of the corpus callosum may show structural degeneration which correlates with fMRI over-activity in the contralesional hemisphere and the degree of motor impairment (Wang et al. 2012; Radlinska et al. 2012). Though the structural T1 and T2 scans did not show lesions to the corpus callosum in any of our patients, we nevertheless ensured that the correlations between AMT and interhemispheric connectivity remained significant when correcting for CST damage or lesion volume. Wang and colleagues (2012) showed that a stroke lesion along the CST impacts on the entire tract due to anterograde and retrograde Wallerian

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Impact on motor function

Fig. 5 Synopsis of the relations between CST damage, changes in cortical excitability, effective connectivity within the cortical motor network and motor function of the affected hand in chronic stroke (asterisk: significant correlation). The direction of changes observed in severely impaired patients is indicated by red arrows. Note that partial correlation analyses and multiple linear regression analyses suggested an additive impact of changes in AMT, the degree of CST damage and disinhibition of the contralesional M1 on individual motor impairment

degeneration. Therefore, an alternative explanation lies in secondary changes in M1 integrity triggered by the lesion to the CST, which might also account for the relationship between reduced interhemispheric inhibition and cortical excitability of the ipsilesional hemisphere (Fig. 4). Neuropharmacological TMS studies reported motor thresholds to be elevated following modulation of axonal excitability via sodium channel blockage by anticonvulsive drugs (e.g., carbamazepine, lamotrigine), yet unaffected by GABAergic interventions (Ziemann et al. 1996; Ziemann 2011). Hence, the AMT seems to primarily dependent on the excitability of axons (Paulus et al. 2008). Therefore, an increased AMT of the ipsilesional hemisphere (as found in the present study in stroke patients with stronger motor deficits) might not only result from disruption of CST signal conduction due to the lesion, but also from secondarily altered axonal properties of M1 neurons. Possibly, lesion-induced changes in axonal excitability might also affect the output of ipsilesional M1 onto the contralesional hemisphere, resulting in reduced interhemipsheric M1–M1 inhibition (Fig. 4). Since the SMA contributes fibers to the corticospinal tract (Classen et al. 1997; Dum and Strick 1991; Newton et al. 2006), it may be affected by secondary degeneration in a similar way as ipsilesional M1. This might explain the AMT-related reduction in inhibitory drive exerted from ipsilesional SMA onto contralesional M1 in patients, contributing to the disinhibition of contralateral M1 (Fig. 4).

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The described scenario might account for the fact that AMT (as index for M1 excitability) explained much more variance of motor impairment (73 %) than the amount of structural CST damage (51 %), as it combines information on the degree of structural damage and subsequent cortical reorganization. Our data thus offer an explanation for the inter-related effects of these factors on recovery of motor function. Accordingly, stroke leads to dysfunction of M1 neurons (e.g., by secondary degeneration or changes in axonal excitability), which causes reduced movementrelated interhemispheric inhibition. This, in turn, results in task-dependent over-activity of the contralesional hemisphere, which is paralleled by enhanced excitability of contralesional M1 neurons (i.e., decreased AMT). These effects might contribute to the motor deficit observed in stroke patients, as suggested by our multivariate regression analysis (Fig. 5). Limitations The cross-sectional design of the present study did not allow us to investigate mechanisms of recovery (which, by definition, always require longitudinal designs). However, our primary goal was to elucidate putative neural factors that influence motor function in the reorganized motor system (i.e., in chronic stroke). Therefore, future studies can now test whether the factors identified in the present study (combination of AMT, CST damage and DCM of interhemispheric connectivity) also have a predictive value in the acute post-stroke phase for long-term recovery. Given the cross-sectional design of the study, we cannot disentangle stroke-induced changes on cortical excitability and connectivity from behaviorally induced effects resulting from inactivity (‘‘learned non-use’’) and their interaction with handedness. However, none of our patients suffered from a severe hand motor deficit which might have resulted in complete inactivity of the affected hand (see Table 1). Moreover, there is evidence that already in acute stroke patients cortical excitability of the lesioned hemisphere is significantly reduced (see, e.g., Talelli et al. 2006), an observation which cannot be explained by a history of decreased limb utilization. Furthermore, increasing cortical excitability within the ipsilesional hemisphere of chronic stroke patients via non-invasive brain stimulation has been shown to beneficially impact on motor function, suggesting an important influence of cortical excitability on motor hand function (Kim et al. 2006; Ameli et al. 2009). However, the exact neurophysiological mechanisms underlying changes in cortical excitability (i.e., AMT) are still poorly understood (Paulus et al. 2008; Ziemann 2011) and, therefore, limit the interpretation of

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our findings. In fact, a number of studies reported inconsistent data on AMT changes in chronic stroke, with a spectrum ranging from elevated to decreased AMTs in well-recovered stroke patients (Byrnes et al. 2001; Thickbroom et al. 2002). The current study demonstrates that AMT reflects several aspects of stroke-induced disturbances at the neural level (subcortical CST damage, cortico-cortical connectivity), which might explain why this parameter showed the strongest correlation with clinical impairment. Although this interpretation also helps to reconcile the heterogeneity found in the literature on AMT changes in different samples of patients, the neurobiological underpinnings of TMS parameters still remain to be elucidated in the future. Another limitation of the present study is the heterogeneity of our sample of stroke patients which differs in lesion size and localization, age and motor impairment. However, the fact that highly significant correlations were evident despite this heterogeneity corroborates the importance of CST damage, changes of cortical excitability and interhemispheric interaction on motor outcome of chronic stroke patients. One might argue that it would have been more adequate to use electrophysiological measures of cortical connectivity, e.g., as assessed by the interhemispheric inhibition (IHI) protocol (Ferbert et al. 1992). However, there are two important limitations regarding IHI: (1) IHI can only be assessed during rest or movement initiation (Ferbert et al. 1992; Murase et al. 2004) and not during motor task execution; (2) evaluation of IHI involves signal conduction through the CST (as IHI is determined on the basis of MEP amplitudes), and hence does not represent an independent marker of cortico-cortical connectivity. DCM of fMRI data overcomes these limitations. However, also DCM constitutes an indirect measure of neuronal connectivity, and hence validation experiments are now needed to reveal the neurophysiological correlates of DCM coupling estimates. Of note, Boudrias and colleagues (2012) have already demonstrated that DCM coupling parameters of interhemispheric inhibition significantly correlated with IHI assessed with TMS, providing face validity of DCM, at least for the motor system. Finally, individual hand dominance in relation to the side of the lesion might impact on interhemispheric M1interaction. However, in healthy subjects, connectivity analyses using TMS or fMRI-DCM have thus far not found a systematic effect of hand dominance on interhemispheric M1-interaction (Nelson et al. 2009; Pool et al. 2013). Furthermore, in the present study, when removing the three patients with left hemispheric stroke and the left-handed patient (leaving 8 right-handed patients with right hemispheric stroke), we still get very similar results, i.e., significant correlations between motor impairment of the

affected hand and interhemispheric inhibition (n = 8: r = -0.856, P = 0.007), as well as the AMT of the ipsilesional hemisphere and interhemispheric inhibition (n = 8: r = 0.723, P = 0.043). This means that our findings were not driven by a particular subgroup of patients. However, the sample size of the present study is too small to draw a final conclusion whether the interaction of handedness and lesion side has any systematic impact on interhemispheric inhibition. Conclusion and therapeutic implications Finally, our findings have implications for novel therapeutic approaches that aim at modulating cortical excitability following stroke to support recovery of function. Currently, two strategies are pursued: (1) enhancing excitability of the lesioned hemisphere, or (2) suppressing excitability of the unaffected hemisphere. There is no clear evidence that one strategy is superior over another (Nowak et al. 2009; Adeyemo et al. 2012), and there is even increasing evidence that not all patients profit from such interventions (for a recent review see Grefkes and Fink 2012). At present, interindividual differences in the response to interventions observed in various studies seem to arise from individual factors that determine the susceptibility to plasticity-inducing rTMS or tDCS protocols (Cheeran et al. 2008; Hamada et al. 2013; Cardenas-Morales et al. 2013). Therefore, ‘‘biomarkers’’ are needed that may help to stratify patients according to their responsiveness to a particular type of treatment. Our study shows that the ipsilesional AMT is not only informative with regard to corticospinal damage but also conveys information on interhemispheric connectivity closely related to behavioral performance. We can only speculate whether patients with increased ipsilesional AMTs might be especially suited for excitability-enhancing brain stimulation over the ipsilesional hemisphere, compared to patients featuring less reduction of cortical excitability. This issue needs to be clarified in future studies. Furthermore, future studies should investigate how brain stimulation impacts on the relationship between AMT, interhemispheric connectivity and motor performance. Acknowledgments CG is supported by the German Research Foundation (DFG GR 3285/2-1). SBE acknowledges funding by the Helmholtz Initiative on Systems-Biology ‘‘The Human Brain Model’’ and the NIH (R01-MH074457). GRF gratefully acknowledges additional support from the Marga and Walter Boll Stiftung.

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