Neural correlates of acute apraxia: Evidence from lesion data and functional MRI in stroke patients

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Behavioural Neurology

Neural correlates of acute apraxia: Evidence from lesion data and functional MRI in stroke patients Andrea Dressing a,b,c,*, Christoph P. Kaller a,b,c, Kai Nitschke a,b,c, Lena-Alexandra Beume a,b, Dorothee Kuemmerer a,b, Charlotte S.M. Schmidt a,b, Tobias Bormann a,b, Roza M. Umarova Karl Egger b,d, Michel Rijntjes a,b, Cornelius Weiller a,b,c and Markus Martin a,b,c

a,b

,

a

Dept. of Neurology and Neuroscience, University Medical Center Freiburg, Freiburg, Germany Freiburg Brain Imaging Center, University of Freiburg, Freiburg, Germany c BrainLinks-BrainTools Cluster of Excellence, University of Freiburg, Freiburg, Germany d Dept. of Neuroradiology, University Medical Center Freiburg, Freiburg, Germany b

article info

abstract

Article history:

Behavioral deficits after stroke like apraxia can be related to structural lesions and to a

Received 8 October 2018

functional state of the underlying network e three factors, reciprocally influencing each

Reviewed: 4 December 2018

other. Combining lesion data, behavioral performance and passive functional activation of

Revised 28 February 2019

the network-of-interest, this study aims to disentangle those mutual influences and to

Accepted 7 May 2019

identify 1) activation patterns associated with the presence or absence of acute apraxia in

Action editor Gereon Fink

tool-associated actions and 2) the specific impact of lesion location on those activation

Published online 18 May 2019

patterns.

Keywords:

paradigm with observation of tool-related actions during the acute phase after first-ever

Apraxia

left-hemispheric stroke (4.83 ± 2.04 days). Behavioral assessment of apraxia in tool-

Tool-use

related tasks was obtained independently. Brain activation was compared between pa-

Brain reorganization

tients versus healthy controls and between patient with versus without apraxia. Interac-

fMRI

tion effects of lesion location (frontal vs parietal) and behavioral performance (apraxia vs

Left-hemisphere stroke

no apraxia) were assessed in a 2
2 factorial design.

Brain activity of 48 patients (63.31 ± 13.68 years, 35 male) was assessed in a fMRI

Action observation activated the ventro-dorsal parts of the network for cognitive motor function; activation was globally downregulated after stroke. Apraxic compared to nonapraxic patients showed relatively increased activity in bilateral posterior middle temporal gyrus and middle frontal gyrus/superior frontal sulcus. Altered activation occurred in regions for tool-related cognition, corroborating known functions of the ventro-dorsal and ventral streams for praxis, and comprised domain-general areas, functionally related to

Abbreviations: ACC, anterior cingulate cortex; ATL, anterior temporal lobe; IFS, inferior frontal sulcus; IPL, inferior parietal lobe; MCA, middle cerebral artery; MFG, middle frontal gyrus; mdLF, middle longitudinal fascicle; MTG, middle temporal gyrus; pMTG, posterior middle temporal gyrus; IFG, inferior frontal gyrus; BORB, Birmingham object recognition test; NIHSS, National Institute of Health Stroke Scale. * Corresponding author. Dept. of Neurology and Neuroscience, University Medical Center Freiburg, Breisacherstr. 64, D-79106 Freiburg, Germany. E-mail address: [email protected] (A. Dressing). https://doi.org/10.1016/j.cortex.2019.05.005 0010-9452/© 2019 Elsevier Ltd. All rights reserved.

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cognitive control. The interaction analyses revealed different levels of activation in the left anterior middle temporal gyrus in the ventral stream in apraxic patients with frontal compared to parietal lesions, suggesting a modulation of network activation in relation to behavioral performance and lesion location as separate factors. By detecting apraxia-specific activation patterns modulated by lesion location, this study underlines the necessity to combine structural lesion information, behavioral parameters and functional activation to comprehensively examine cognitive functions in acute stroke patients. © 2019 Elsevier Ltd. All rights reserved.

1.

Introduction

Understanding the neural correlates of deficits in higher cognitive functions after acute stroke can help to clarify the neural basis of the impaired function and to identify early adaptive changes that promote recovery. The challenge in studying the loss of higher cognitive functions and recovery of acute post-stroke deficits arises from the entanglement of a complex network-based functional organization of higher cognitive functions and heterogeneous focal structural damage of network-integrity due to individual stroke lesions. By relating patients' behavioral performance to the site of the stroke lesion, critical brain regions for higher cognitive function can be identified on the structural level, by applying the method of lesion-symptom analytic techniques (Bates et al., 2003). Beside lesion-symptom associations, changes in task-specific and domain-general functional network activation, related to behavioral performance, characterize poststroke deficits and improve understanding of early adaptive brain changes that facilitate recovery. Support for this assumption provide studies in stroke patients, where taskspecific upregulation within the remaining parts of the taskspecific network (Grefkes & Ward, 2014; Rijntjes, 2006; Saur et al., 2006; Umarova et al., 2016; Ward, Brown, Thompson, & Frackowiak, 2003; Weiller, Ramsay, Wise, Friston, & Frackowiak, 1993), perilesional or contra-lesional activation (Crinion & Price, 2005; Heiss & Thiel, 2006) and a compensatory activation of domain-general areas (Brownsett et al., 2014; Geranmayeh, Brownsett, & Wise, 2014, Geranmayeh, Chau, Wise, Leech, & Hampshire, 2017) could be identified in the early stage after stroke. However, the existing studies, examining functional brain activation in relation to behavioral performance are limited by not taking into account the location of the lesion within the network and therefore cannot attribute the observed pattern of activation to a specific lesion site. Therefore, our study is based on the hypothesis that a comprehensive description of patients' phenotype after acute stroke should be based on a combination of the functional status of the cognitive network-of-interest, the structural information of the stroke lesion within the network and their relation to the behavioral deficit (Fig. 1A). In particular, given the anatomical underpinnings of network architecture, the location of the structural lesion within the network might influence functional activation patterns. Each lesion differentially affects strategical hubs and connecting fiber tracts,

determines the extent and location of the remaining parts of the network, and therefore influences the capacity to involve functionally related and structurally preserved areas. We address this hypothesis in acute post-stroke apraxia, which affects patients' ability to manipulate tools and to perform skilled actions, despite intact sensorimotor and coordinative capacities (Goldenberg, 2014; Haaland, Harrington, & Knight, 2000; Leiguarda & Marsden, 2000; Rothi, Ochipa, & Heilman, 1991). Lesion studies correlate tool use in different modalities (e.g., pantomiming tool use, imitation of tool related gestures, actual tool use) to cortical key nodes within the left hemisphere, comprising left inferior frontal gyrus (IFG) (Buxbaum, Shapiro, & Coslett, 2014; Manuel et al., 2013; Mengotti et al., 2013; Weiss et al., 2016), inferior parietal lobe (IPL) (Binkofski & Buxbaum, 2013; Buxbaum et al., 2014, Buxbaum, Kyle, Grossman, & Coslett, 2007; Dressing et al., 2018; Goldenberg, 2009; Hoeren et al., 2014; Martin et al., 2017; Mengotti et al., 2013; Orban & Caruana, 2014) and temporal lobe (Buxbaum et al., 2014; Finkel, Hogrefe, Frey, Goldenberg, & Randerath, 2018; Goldenberg & Randerath, 2015; Hoeren et al., 2014; Mengotti et al., 2013). Further, within one modality of toolrelated actions (e.g., pantomime) different error types could be related to different lesion locations (Hoeren et al., 2014; Manuel et al., 2013). Lesion studies, however, partly draw a heterogenous picture of lesion correlates and relate the same deficit (e.g., pantomime) to different lesion locations (IFG, IPL, temporal lobe) (Goldenberg & Randerath, 2015). Integrating data from functional MRI investigations in healthy subjects (Bohlhalter et al., 2009; Brandi et al., 2014; € rfer et al., Chaminade, Meltzoff, & Decety, 2005; Hermsdo 2001; Johnson-Frey, Newman-Norlund, & Grafton, 2005; Muehlau et al., 2005; Vingerhoets, Acke, Vandemaele & Achten, 2009), knowledge about structural and functional connections between parietal, frontal and temporal areas key nodes (Caspers et al., 2011; Catani et al., 2012; Garcea & Mahon, 2014; Makris et al., 2013; Makris & Pandya, 2009; Ruschel et al., 2014) and lesion data a network model with three parallel processing streams has been proposed. This network is discussed as the basis for action cognition (Binkofski & Buxbaum, 2013; Martin, Beume, et al., 2016) and other cognitive domains like language (Fridriksson et al., 2016; Hickok & Poeppel, 2007; Saur et al., 2008; Klein, Moeller, Glauche, Weiller & Willmes, 2013) or spatial attention (Corbetta and Shulman, 2011; Umarova et al., 2011).

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Fig. 1 e Theoretical model and overview over analyses. We suggest that a comprehensive characterization of acute stroke patients should be based on the structural information about site of the stroke lesion, the behavioral deficit and the functional status of the cognitive network-of-interest, which together promote the patient's phenotype (A). In our study, we assessed those three components in acute post-stroke apraxia (B). Functional activation of the praxis network (by passive network activation) was compared between stroke patients and healthy controls (1). Secondly, the dependence of functional activation on the presence or absence of a behavioral deficit was assessed (2), representing the neural network correlates of acute apraxia. The third analysis comprised all three components by examining functional brain activation in an interaction model of lesion location (frontal vs parietal) and behavioral performance (deficit vs no deficit) (3). Additionally, the analyses were completed by voxel-based lesion-symptom mapping specifying lesion locations, associated with behavioral deficits (4).

Within the network, the ventro-dorsal stream connects higher order visual areas (MT/V5þ), the IPL (angular and supramarginal gyri), the ventral premotor cortex and IFG via long-distance association fibers, running above the sylvian fissure and including the superior longitudinal fascicle (SLF) III and the arcuate fascicle (Binkofski & Buxbaum, 2013; Catani, Jones, & Ffytche, 2005; Hamzei et al., 2016; Kreher et al., 2008; Vry et al., 2012; Rizzolatti & Matelli, 2003). Below the sylvian fissure the ventral stream is formed by fibers, traversing through the extreme capsule, inferior frontooccipital fascicle and uncinate fasciculus, connecting inferior, middle and superior temporal cortices as well as IPL to anterior IFG (Catani et al., 2005, 2002; Garcea & Mahon, 2014; Makris & Pandya, 2009; Vry et al., 2012; Vry et al., 2015). In relation to tool and object cognition, the ventro-dorsal stream may support skilled actions and known motor programs, facilitates handling of known tools and encodes spatiotemporal aspects of actions (Binkofski & Buxbaum, 2013; Dressing et al., 2018; Hoeren et al., 2014; Niessen, Fink, & Weiss, 2014). The ventral stream, conversely, is linked to conceptual and semantic aspects of tool-use (Heilman, Rothi & Valenstein, 1982; Rijntjes, Dettmers, Bu¨chel, Kiebel, Frackowiak & Weiller, 1999; Buxbaum et al., 2007; Niessen et al., 2014; Vry et al., 2015), and might overlap with areas, relevant for communicative tasks (Finkel et al., 2018). Akin to the network idea of tool-related cognition and against the background of the introduced triangle (Fig. 1), we hypothesize to detect deficit-related network activation patterns, which are not necessarily related to a distinct lesion site. We hypothesize that patients with presence of limb apraxia in tool-related tasks show dysfunction of mainly the ventro-dorsal stream and depict distinct activation patterns compared to stroke-controls. Secondly, we postulate that

lesion location mediates those early changes in network activation, as, depending on the location of the lesion, different parts of the network and underlying streams are affected. The precise anatomical knowledge of the processing of tool-related information in the ventro-dorsal and additionally in the ventral stream allows to define circumscriptive brain regions in key areas (hubs) of the network. We study brain activation in relation to a parietal and a frontal lesion cluster, affecting the beginning or input (parietal) and the termination (frontal) of the ventro-dorsal and ventral stream, respectively. To investigate these hypotheses, the study uses a functional MRI paradigm with passive action observation to examine brain activation in relation to preserved or impaired action cognition and lesion location. Independently, behavioral performance in tool-related tasks was assessed with established tests for pantomime, imitation of tool use and actual tool use, which allow a detailed characterization of behavioral deficits. Advantage of analyzing fMRI activity, elicited by passive action observation, is that the network can be examined in a performance-independent manner and extent and magnitude of the fMRI activity are independent from subjects' adherence to any kind of behavioral task within the scanner. Therefore, fMRI signal can be interpreted as a direct marker of the different regions' functional state with respect to action cognition. Further, fMRI paradigms, using active motor paradigms (e.g., Brandi et al., 2014; Gallivan, McLean, Valyear, & Culham, 2013; Hamzei et al., 2016) might be too challenging for patients during the first days after stroke and are susceptible for movement artifacts. The same experimental approach has already been successfully used in a cohort of chronic stroke patients (Martin, Nitschke, et al., 2016) and similar strategies were used in language

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experiments, where activity, elicited by a perceptive task (listening to language) was correlated with active speech production to elucidate adaptive activity changes in stroke patients with aphasia (Saur et al., 2006). In a large sample of acute stroke patients (n ¼ 48) with firstever left-hemispheric stroke we first compared passive brain activation, elicited by action observation, between patients and healthy controls. Secondly, brain activation was assessed in impaired versus preserved performance in tool-associated tasks. Thirdly, functional brain activation was assessed in an interaction model between lesion location (frontal vs parietal) and the behavioral performance (deficit vs no deficit) (Fig. 1B). Importantly, our study is the first, which controls for potential confounds (age and lesion size) on functional brain activation using a matched-pairs approach (Kaller et al., 2014) while assessing brain activation in acute stroke patients.

2.

Material and methods

In this section we report how we determined our sample size, all data exclusions, all inclusion/exclusion criteria, whether inclusion/exclusion criteria were established prior to data analysis, all manipulations, and all measures in the study.

2.1.

Subjects

Patients were consecutively recruited from the Department of Neurology and Clinical Neuroscience at the University Medical Centre Freiburg. During 05/2013 to 04/2015 all patients with an acute stroke were screened for eligibility to participate in the study. Inclusion criteria were (i) first-ever acute ischemic stroke in the area of the left middle cerebral artery (MCA) and (ii) ability to tolerate a 30 min MRI-scan and to perform the fMRI task. Presence of apraxia was no inclusion criterion. Exclusion criteria, as reported previously (Dressing et al., 2018; Hoeren et al., 2014) comprised (i) concurrent disorders (e.g., major cognitive impairment, dementia, previous brain injury); (ii) structural brain changes (e.g., extensive white matter changes); (iii) hemodynamic alterations (e.g., carotid occlusion with insufficient collateralization); (iv) severe visual impairments including hemianopia. 62 Patients were included in the study. Two patients had to be excluded due to severe vigilance problems during the behavioral testing and another eight patients due to insufficient alertness or deviant performance in the control-fMRI task (button press upon appearance of a white circle during the videos, see below). Exclusion resulted from response rate < 80% compared to the average performance of patients or false alarms > 120% (defined as mean response latency ± 2 SD in relation to the current run or additional responses). Further, patients with small lesions (<1.5 cm3; n ¼ 3) were excluded retrospectively. Finally, one patient had to be excluded because of technical difficulties with fMRI data processing. Taken together, the present analyses are based on valid data of 48 patients. Three of these patients had an occlusion of the left internal carotid artery, with sufficient collateralization as documented by ultrasound. A control-group of 29 aged healthy volunteers (mean ± SD, 72 ± 7 years, 15 male) without neurological or psychiatric premorbidity provided control fMRI data and normative

behavioral data for apraxia tests as reported previously (Hoeren et al., 2014). Full written consent according to the declaration of Helsinki was obtained from all patients or their legal guardian and from all control subjects. The study was approved by the local ethics authorities (University of Freiburg, Germany). The study procedures or analyses were not pre-registered prior to the research.

2.2.

Clinical and behavioral testing

Pantomime and imitation were tested using a modified version of the test battery by Bartolo, Cubelli, and Della Sala (2008), also see (Dressing et al., 2018; Hoeren et al., 2014). Patients were asked to mime the use of 14 common tools (e.g., hammer, pen) depicted as line drawings (pantomime of object use) and to imitate 10 tool-associated gestures demonstrated by the examiner (imitation of object use). Pantomime of object use was performed without the presence of an object with the contralesionally right hand, comparable to previous studies (Goldenberg, Hartmann, & Schlott, 2003; Goldenberg & Randerath, 2015; Hoeren et al., 2014), as most patients are highly accustomed to use their right hand in everyday actions. Patients were prompted to use the left (ipsilesional) hand in cases of right-arm paresis or fine motor coordination deficits (which were tested beforehand with the NIHSS motor score and the 9-hope-peg test) to avoid interfered with performance. Eight patients presented with a relevant paresis of the right arm, all of them performed the task with the left hand. Of the remaining 40 patients, 35 performed the pantomime-task with the right hand. Testing of actual tool use was performed according to Goldenberg and Spatt (2009). A rack with five fixed items (tool recipients e.g., nail, screw) and e one after another e five corresponding tools (e.g., hammer, screw driver) were presented to the patient. Patients were instructed to demonstrate the use of the tool on the suitable recipient with their contralesionally hand. The correct recipient was indicated by the examiner if a patient was unable to select the matching recipient. Separate scores for recipient selection (tool selection; 2 points for prompt correct recipient selection, 1 point for correct selection after a period of hesitation or trial and error) and action execution (tool execution; 2 points for the correct application of the tool, 1 point for success after trial and error or hesitation) were obtained (Martin et al., 2016). Items of the testing battery for all behavioral tests and information about scoring are listed in Supplementary Methods S1. Furthermore, all patients completed the Token Test of the Aachen Aphasia Test (AAT) (Huber, Poeck, & Willmes, 1984) and a subtest of the Birmingham object recognition test (BORB subtest 11) (Riddoch & Humphreys, 1993), to assess language comprehension and to assure sufficient object-recognition capacities, respectively. In addition, all patients performed the Corsi block-tapping test for short-term and working memory (Kessels, van Zandvoort, Postma, Kappelle, & de Haan, 2000, 2008). Assessment of stroke severity was based on the National Institute of Health Stroke Scale (NIHSS).

2.3.

Scoring and evaluation of behavioral data

The performances of patients and controls were videotaped and scored separately by two independent raters (M.M. and A.D.)

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with one rater being blind to location and extent of the stroke. Subjects who declined videotaping (one patient) or could not be recorded for technical reasons (one control subject) were scored directly by the examiner, who was trained in the scoring system. For apraxia tests interrater agreement was moderate to almost perfect in terms of Cohen's k (pantomime of object use k ¼ .44 (p < .001); imitation of object use k ¼ .48 (p < .001); tool selection k ¼ .79 (p < .001); tool execution k ¼ .90 (p < .001)). In order to compare activity in patients with and without behavioral deficit after left-hemispheric stroke, scores of all behavioral tests were binarized. Deficient performance in a task was defined as a score below the 5th percentile of the behavioral test results of the 29 healthy control subjects (Cutoff scores: pantomime of object use 11/14, imitation of object use 8/10, tool selection 8/10, tool execution 9/10). The presence of an apraxic deficit was defined as performance below the 5th percentile compared to the controls in at least one apraxia tests.

2.4.

Lesion delineation and separation

Stroke lesions were delineated on structural MR-images. For specifications of the structural imaging data see Supplementary Methods S2. The process of lesion delineation was performed as previously reported (Dressing et al., 2018; Hoeren et al., 2014; Martin et al., 2016). Ischemic lesions were first delineated on the DWI based on intensity thresholds using a customized region-of-interest toolbox implemented in SPM8 (http://www.fil.ion.ucl.ac.uk/spm/software/spm8) and further refined manually. Exact correspondence between lesion map and lesion was subsequently checked by independent observers (M.M., L.B., A.D.). Lesion mapping and inspection were performed in MRICron (https://people.cas.sc. edu/rorden/mricron/install.html). For normalization, the diffusion-weighted images and corresponding lesion maps were co-registered to the anatomical T1w scan which was segmented using the VBM8 toolbox (r435; http://dbm.neuro. uni-jena.de/vbm/download/). Deformation field parameters for nonlinear normalization into the stereotactic Montreal Neurological Institute (MNI) standard space were obtained using the DARTEL approach (diffeomorphic anatomical registration through exponentiated lie algebra) (Ashburner, 2007) implemented in VBM8. Following normalization, the individual lesion maps were again inspected and compared to the lesions in native subject space to ensure that the extent of the ischemic damage was accurately delineated in MNI-space. Lesions were separated into four groups according to the overlap of the individual lesion with the regions of the AAL atlas (Tzourio-Mazoyer et al., 2002), and in relation to anatomical landmarks. First, lesions located in the frontal lobe (n ¼ 16) sparing temporo-parietal areas, and secondly, lesions located in the parietal lobe sparing the frontal lobe (n ¼ 15) were defined. The third and fourth group comprised subcortical lesions (n ¼ 8) and large lesions, covering frontal and parietal or temporal areas (n ¼ 9).

2.5.

Functional MRI paradigm

The functional MRI paradigm comprised action observation of typical tool-associated actions (Hamzei et al., 2016; Martin

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et al., 2016b). 48 video sequences with a length of 4 sec each presented tool-associated actions from a first-person perspective (e.g., washing hands with a brush; sweeping up shards with a hand broom; mashing potatoes; hammering in a nail, see Supplements S3 for all items). Actions were performed with the right hand, the left hand was only used for e.g., holding a tool recipient (Fig. 2). Sequences were recorded with a Panasonic V100 camcorder (Panasonic Corporation, Kadoma, Japan). Eight further video sequences were used for a pre-test outside of the scanner. The paradigm consisted of two runs. Each run contained 16 blocks with three video sequences (Condition: Video). Videos were separated by 1 sec intervals; blocks were separated by jittered intervals (9.7e25.2 sec) based on in-house efficiency optimization algorithms. Both runs were identical regarding timing and stimuli, but the order of the videos varied. The arrangement of the video sequences aimed at maximized distances between semantically-related tools (e.g., hammer and screwdriver), and between similar movements (e.g., sawing and cutting bread). Total length of each run was 459 sec. To monitor attention during the scan, subjects had to respond by button press to the occurrence of a large white circle (Condition: Circle; see Fig. 2). The circle appeared 32 times in each run with a duration of 400 msec. To avoid interference with the recognition of the tool-associated action, the circle was shown at least 1.3 sec after beginning of the video sequence, but no later than 400 msec before the end of the video to prevent that the change from video to background interfered with the attention task. In this time window, timing of the attention events was specified according to a Gaussian distribution (mean ± SD after start of the video, 3.5 sec ± 1.1 sec). A total of twenty-two attention events were timed during videos and ten in the between-block intervals. In-house algorithms were used to make the circle appearances completely independent from the videos so that during subsequent analyses, the variance in blood oxygen level-dependent (BOLD) signal to either Circle or Video conditions could be unambiguously determined. Functional MRI scans were acquired on a 3 T Siemens TRIO system (Siemens, Erlangen, Germany). Subjects lay supine in the scanner with the head fixed with pillows in an 8-channel head coil. Stimulus presentation was controlled by the Presentation software (version 16; Neurobehavioral Systems Inc, Berkeley, CA; https://www.neurobs.com). Stimuli were projected on a screen (screen resolution of 800
600 pixels), mounted on the rear of the scanner bore, and were viewed via a mirror system. Sound of the videos was binaurally presented with MR-compatible pneumatic headphones (MR confon, Magdeburg, Germany). A MR-compatible button device, held in the ipsilesional hand, was used for stimulus response. A total of 306 scans per run was acquired using a gradient echo echoplanar imaging (EPI) T2*-sensitive sequence. A highresolution T1 anatomical scan (see Supplement S2) was registered for spatial normalization of fMRI. The copyright to the paradigm is held by MM and CW, who choose to invoke legal copyright restrictions as a basis for not making the code freely available in a public repository. Readers seeking access to the paradigm should contact the copyright holder or the corresponding author (AD). The material will be released to named requestors under a right of use agreement for free.

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Fig. 2 e Illustration of the functional MRI paradigm. Exemplary video-block of the action observation paradigm. Each block consisted of three videos of 4 sec duration, separated by 1 sec intervals (condition Video), presenting different toolassociated actions. The video presentation was audio-visual; patients observed videos on a screen in the scanner and listened to the sounds elicited by the actions via pneumatic headphones. To ensure attention to the screen, subjects had to respond by button press to intermittent brief appearances of a white circle (condition Circle; see third image, second column). The picture was adopted from Martin et al. (2016).

2.6. Data processing and first-level functional MRI analysis Pre-processing and first-level functional MRI analyses were performed using SPM8 (http://www.fil.ion.ucl.ac.uk/spm/ software/spm8). Given that in-house algorithms were applied for online correction of motion and distortion artifacts (Zaitsev, Hennig, & Speck, 2004), functional images were already unwarped and spatially aligned before pre-processing. Motion-corrected EPI sequences were despiked with the ArtRepair toolbox (v4; http://cibsr.stanford.edu/tools/humanbrain-project/artrepair-software.html) and co-registered to the subjects' anatomical T1-scans. Normalization parameters were obtained using the DARTEL (diffeomorphic anatomical registration through exponentiated lie algebra) (Ashburner, 2007) approach implemented in the VBM8 toolbox (r435, http://www.neuro.uni-jena.de/vbm/download/). First-level estimates of hemodynamic activation changes were computed based on the general linear model implemented in SPM8. The respective regressors for each trial type (Condition Video and Circle) were built by convolving the canonical hemodynamic response function with either boxcar functions indicating onsets and offsets of the videos, or stick functions indicating the onsets of the circle presentations. Head-motion parameters and their first derivatives, as well as first to fourth order polynomial regressors of slow drift, were entered as nuisance regressors. Low-frequency components of functional MRI time series were removed by standard 128 sec high-pass filtering. The functional MRI activity elicited by watching toolassociated actions and by responding to the appearance of the white circle was defined by the contrasts for the Video and Circle conditions (against implicit rest). Contrasts between the two conditions (e.g. Video > Circle) were not calculated. We used a post-statistics normalization approach in which the

first-level analyses were conducted in individual space and resulting beta images for Video and Circle were transformed into stereotactic MNI space using the normalization parameters from the anatomical scans (Poldrack, Mumford, & Nichols, 2001). Normalized beta images were resampled to a spatial resolution of 1.5
1.5
1.5 mm, smoothed with an isotropic Gaussian kernel with a full-width at half-maximum of 9 mm, recommended in the literature (Ball et al., 2012; Chen & Calhoun, 2018; Hopfinger, Bu¨chel, Holmes, & Friston, 2000; Mikl et al., 2008) and previously used in fMRI analyses, regarding action cognition or spatial attention in stroke patient (Beume et al., 2015; Bohlhalter et al., 2009; Martin et al., 2016), and averaged for further analyses. As the smoothing kernel can influence the spatial specificity of results (Ball et al., 2012) a second smoothing with a Gaussian kernel with a fullwidth at half-maximum of 4.5 mm was performed, results reveal identical peak activation cluster and are presented in Supplementary Table S4. Activity levels (beta-values) were extracted from the peak activation clusters (6 mm sphere). A ttest against zero was performed to determine a significant difference from implicit rest for the cluster. Anonymized data supported the claims in this paper cannot be publicly archived due to ethical restrictions. Researchers seeking access to the data should contain the corresponding author [AD] who is responsible for considering and granting access requests. Access can be granted only to named individuals in accordance with ethical procedures governing the reuse of sensitive clinical data.

2.7. Second-level functional MRI analyses and multidimensional matching Second-level analyses were computed using GLM Flex2 (2014) (http://mrtools.mgh.harvard.edu/index.php/GLM_Flex) based on Matlab (release 2012a). An overview over the analyses is

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given in Fig. 1B. Information concerning the analysis codes is provided in the Supplement. To evaluate whole brain activity elicited by the task, onesample t-tests were performed for each condition (Video and Circle) in patients and controls separately. Differences between patients and controls were determined for each condition using two-sample t-tests (task-related activation).

was computed. In this approach, no matching was performed, but the multiple regression analysis allowed controlling for lesion volume and age, which were added as nuisance variables to the model. The analysis and visualization were conducted using custom code for SPM 8, for a detailed description of interaction effects in multiple regression analyses see Kaller et al. (2012).

2.8.

2.11.

Factorial approach

To analyze the impact of a behavioral deficit on brain activity, patients were separated according to the presence (Deficit) or absence (NoDeficit) of a limb apraxia in tool related tasks. Furthermore, sub-analyses were performed for each binarized behavioral score. For group comparisons two-sample t-tests on whole brain activity were performed. Second, patients were separated according to the location of the lesion (frontal vs parietal) and again according to behavioral measures (with vs without presence of a behavioral deficit), resulting in four groups. Patients with large or subcortical lesions were excluded in this part of the analysis. The interaction effect between test performance (deficit vs no deficit) and lesion location (frontal vs parietal) was assessed in a 2
2 factorial ANOVA (analysis of variance). The step-wise approach was taken to account for larger sample sizes and to avoid underpowered analyses for the first question.

2.9.

Multidimensional matching and control analyses

To control for the potential confounding factors age and lesion size in the factorial approach a multi-dimensional matching approach was applied. The idea behind the matching approach is a two-step procedure that accounts for betweensubject differences in a multitude of variables, thereby minimizing potential a priori differences between two groups (Kaller et al., 2014). Based on an in-house software (Kaller et al., 2014) pair-wise Mahalanobis distances were computed between groups in a multi-dimensional space spanned by the selected matching variables (age, lesion volume). Afterwards, pairs of subjects with least distances are recursively identified (distance < 1.2) (Supplement S4 and Supplementary Figure S1). Evaluation of interaction effects was performed in pairs, matched between the group of lesion location with identical numbers for each behavioral performance in each group. Due to the matching approach, the number of matched pairs (deficit vs no deficit, frontal vs parietal, interaction effects) for age and lesion size varied for each analysis. To control for time between stroke onset and fMRI data, which might influence functional activation during the task, we added the variable stroke onset-fMRI-delay as covariate of no interest in the analyses, results are presented in Supplementary Figure S4.

2.10.

Multiple regression analysis with interaction effects

Additionally, we performed a multiple regression analysis with interaction effects to examine the interaction between the variable lesion location and the variable behavioral performance (pantomime of object use, imitation of object use). A model with behavioral performance as continuous variable and lesion location as dichotomous variable (frontal, parietal)

Voxel based lesion symptom mapping

For voxel-based lesion-symptom mapping (VLSM) continuous and dichotomous behavioral measures (i.e., using voxel-wise Brunner-Munzel or Liebermeister tests, respectively), implemented in the non-parametric mapping software distributed with MRIcron (version 12/12/2012) (Rorden, Karnath, & Bonilha, 2007) were used to determine lesioned voxels significantly associated with a behavioral deficit.

2.12.

Data presentation

Results are displayed as surface renderings on an in-house average template of 50 normalized T1 datasets from a sample of healthy subjects who participated in other studies (age mean ± SD 47 ± 20.75, range 22e84 years; 25 male) (Beume et al., 2015) using MRICron. Plots illustrating activity levels at different peak voxels were created using the viewer FIVE distributed with GLM Flex in Matlab (release 2012a). Assignment of functional imaging results to anatomical structures was based on the Automated Anatomical Labeling (AAL) atlas (Tzourio-Mazoyer et al., 2002). All functional MRI results are presented on a threshold of p < .001 uncorrected, to allow comparability to previous fMRI studies in stroke patients (Martin et al., 2016b; Saur et al., 2006; Snaphaan, Rijpkema, Uden, Fernandez, & de Leeuw, 2009; Umarova et al., 2016).

3.

Results

3.1.

Demographical and behavioral data

Testing and functional MRI were conducted in the acute phase after stroke (mean ± SD behavioral testing: 4.5 ± 1.8 days; imaging: 4.8 ± 2.0 days) with a difference between behavioral testing and functional MRI of .3 ± 1.6 days. Table 1 presents an overview of demographical data and behavioral test scores of the 48 patients and of the subsets of patients with behavioral deficit versus without behavioral deficit in object-associated tasks after matching for age and lesion size. Importantly, patients with frontal or parietal lesions did not differ in behavioral performance (see Table 1 for demographical and behavioral data of frontal and parietal stroke, last column for ManneWhitney-U tests). Behavioral and demographical data for matched subgroups for each apraxic deficit are displayed in the Supplements (Supplementary Table S1). No significant difference between symptom onset and behavioral testing was found for the group of frontal versus parietal stroke (Table 1) and for the matched subgroups for each apraxic deficit (pantomime of object use, imitation of object use) (Supplementary Table S1). For the main analysis, however, patients with behavioral deficit were tested significantly later

8

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Table 1 e Demographical and behavioral data. All stroke

Apraxia no deficita

Apraxia deficita

n ¼ 48

n ¼ 17

n ¼ 17

64.88 9.91 46.00 79.00 13/4

66.47 8.28 46.00 78.00 10/7

136.0 p ¼ .786

65.67 12.858 43 79 10/2

67.50 12.41 42 79 9/3

67.5 p ¼ .795

27.81 29.75 1.81 119.90

33.42 24.86 4.23 98.73

115.0 p ¼ .322

31.3825 19.36240 2.70 61.61

28.80 20.32 1.81 63.90

63.0 p ¼ .603

3.76 1.44 2 6

5.06 2.22 1 9

84 p ¼ .038

4.33 2.67 1 9

3.92 1.67 2 6

67.5 p ¼ .799

3.82 1.81 1 7

5.75 1.99 2 10

68.5 p ¼ .008

4.83 2.44 1 10

4.41 1.56 2 7

65.0 p ¼ .713

8.06 4.81 1.00 18.00

10.47 7.17 1.00 24.00

122.5 p ¼ .454

9.50 6.360 2 22

5.25 3.55 1 11

43.5 p ¼ .098

2.35 2.15 .00 8.00

3.65 3.02 .00 9.00

109.5 p ¼ .231

3.08 2.234 1 9

1.50 2.067 0 7

32 p ¼ .018

12.94 .97 11.00 14.00

7.47 4.23 .00 13.00

20.0 p < .001

8.50 4.908 0 14

10.58 2.81 4 14

58 p ¼ .415

9.24 .83 8.00 10.00

7.18 2.07 1.00 10.00

45.0 p < .001

7.50 2.611 1 10

8.17 1.99 4 10

62 p ¼ .554

10.00 .00 10.00 10.00

9.24 1.95 2.00 10.00

102.0 p ¼ .150

9.17 2.290 2 10

9.25 1.28 6 10

65 p ¼ .614

9.65 .70 8.00 10.00

7.06 2.68 2.00 10.00

44.0 p < .001

7.17 2.980 2 10

8.58 2.31 2 10

51 p ¼ .206

31.71 .59 30.00 32.00

31.12 1.41 27.00 32.00

113.5 p ¼ .290

30.75 1.545 27 32

31.50 .67 30 32

52 p ¼ .222

4.47 1.07 3.00 7.00

4.59 1.00 3.00 6.00

127.5 p ¼ .536

4.58 1.311 3 7

5.25 1.06 3 7

49 p ¼ .171

Age (years) Mean 63.31 SD 13.68 Min 21.00 Max 79.00 Gender male/female 35/13 Lesion size (cm3) Mean 31.52 SD 30.23 Min 1.81 Max 140.50 Stroke e test-delay (d) Mean 4.48 SD 1.88 Min 1 Max 9 Stroke e fMRI-delay (d) Mean 4.81 SD 2.07 Min 1 Max 10 NIHSS on admission Mean 7.90 SD 5.89 Min .00 Max 24.00 NIHSS on discharge Mean 2.81 SD 2.57 Min .00 Max 9.00 Pantomime of object use Mean 9.85 SD 4.47 Min .00 Max 14.00 Imitation of object use Mean 7.92 SD 2.33 Min .00 Max 10.00 Tool execution Mean 9.38 SD 1.57 Min 2.00 Max 10.00 Tool selection Mean 8.17 SD 2.62 Min 2.00 Max 10.00 Birmingham object recognition subtest 11 Mean 31.31 SD 1.21 Min 27.00 Max 32.00 Corsi block-tapping test forward Mean 4.52 SD 1.17 Minimum 2.00 Max 7.00

Mann Whitney U

Frontala

Parietala

n ¼ 12

n ¼ 12

Mann Whitney U

9

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Table 1 e (continued ) All stroke

Apraxia no deficita

Apraxia deficita

n ¼ 48

n ¼ 17

n ¼ 17

Corsi block-tapping test reverse Mean 4.33 SD 1.36 Min .00 Max 7.00 Token test points Mean 38.74 SD 16.84 Min .00 Max 50.00 a

Mann Whitney U

Frontala

Parietala

n ¼ 12

n ¼ 12

Mann Whitney U

4.76 .97 3.00 6.00

4.06 1.34 .00 6.00

96.5 p ¼ .099

3.83 1.992 0 6

4.50 .91 3 6

64 p ¼ .628

41.76 15.74 .00 50.00

38.69 15.07 4.00 50.00

101.5 p ¼ .217

37.82 14.98 6.00 50.00

39.67 18.05 .00 50.00

53 p ¼ .415

After matching for the main-analysis.

than patients without deficit (Table 1). A significant difference was found between symptom onset and fMRI for overall apraxia and the subtest pantomime but not for imitation of object use and lesion location, presumably as a result of reduced tolerance for testing and performing the fMRI of patients with presence of limb apraxia. Correlations between test scores and demographical measures of all patients in the study are listed in the Supplement (Supplementary Table S2). The need for adopting the matched-pairs approach to control for confounds (see Section 2) was due to the higher age of the overall sample of n ¼ 48 patients being significantly correlated with lower performance in all apraxia test scores except tool execution, while larger lesion volume was correlated with lower performance in imitation of object use and tool selection. In line with previous studies (Dressing et al., 2018; Hoeren et al., 2014; Martin et al., 2016a), apraxia test scores were significantly intercorrelated (Supplementary Table S2).

3.2.

3.3.

fMRI activation of patients versus healthy controls

Fig. 3B shows overall activity as elicited by the passive action observation paradigm against implicit rest in all stroke patients (n ¼ 48) compared to healthy controls (n ¼ 29). In stroke patients and controls, passive observation of tool-use videos resulted in robust bilateral activation of the fronto-parietal network, which is typically involved in higher cognitive motor functions (Fig. 3B). A hypoactivation was found in regions associated to the default mode network (Horn, Ostwald, Reisert, & Blankenburg, 2014; Laird et al., 2009). Action observation in stroke patients elicited a generally reduced overall network activity compared to healthy controls, presumably as result of the focal brain damage (Fig. 3B). Activity patterns in response to the control task (Circle) were comparable between stroke patients and controls (Supplementary Figure S3).

3.4. Differences in network activation between apraxic and non-apraxic patients

Lesion anatomy

Fig. 3A depicts an overlap image of stroke lesions of all 48 patients. Lesions are located in the territory of the MCA, with a maximum overlap in the insular and adjacent subinsular white matter (20/48 patients). Voxel-based lesion symptom mapping in all patients and in the groups of matched patients revealed no significant results under the conventional threshold of p < .05 FDR corrected. VLSM revealed lesions in the IPL and the superior temporal gyrus, which were significantly associated with apraxia of tool-related gestures (binarized scores) and for different apraxia subscores (continuous scores pantomime of object use, imitation of object use) on the more exploratory threshold of p < .01 uncorrected, which corroborates results from previous VLSM studies on tool-related tasks (Binkofski & Buxbaum, 2013; Dressing et al., 2018; Goldenberg, 2017; Hoeren et al., 2014; nine, Buxbaum, & Coslett, 2010; Martin, Beume, et al., Kale 2016; Niessen et al., 2014) (Supplement Figure S2). Groups of apraxic and non-apraxic patients did not differ in terms of lesion distribution, with lesions covering the entire territory of the MCA with a subcortical maximum, but without obvious clustering in any cortical region (lesion overlaps are presented in Supplement Figure S2).

Fig. 4 depicts the network activation pattern, compared between matched patients with (n ¼ 17) and without (n ¼ 17) apraxia in tool use tasks. Apraxia was defined as performance below cut-off in at least one tool-related task (pantomime of object use, imitation of object use, tool execution, tool selection). Patients with apraxia showed a differential activation compared to those without apraxia in the bilateral posterior middle temporal gyrus (pMTG) and the bilateral frontal lobe, located in the middle frontal gyrus/inferior frontal sulcus (MFG/IFS), superior frontal sulcus (SFS), adjacent to the dorsal parts of IFG and in the anterior cingulate cortex (ACC) (p < .001 uncorrected) (Fig. 4A). The reverse contrast did not reveal any significant results. Peak voxel of each activity cluster in the left hemisphere lay only rarely within the lesion. A total of 2 patients had a lesion in the pMTG (57/55/4), while no patient presented with a lesion to the MFG (27/23/28). The different apraxia subscore (pantomime of object use, imitation of object use) showed different activation patterns, which are related to the action-observation network observed in healthy subjects. Pantomime deficits were associated with a higher activation in the bilateral pMTG, while deficits in the imitation of object use were associated with modulated

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Fig. 3 e Lesion overlap and task-related activation. The overlap of binarized lesions of the 48 patients included in the analysis (A) is covering the entire territory of the middle cerebral artery. In B, regions modulated by the observation of toolassociated actions (video > rest) are depicted. During observation of tool-related videos a robust activation of the frontoparietal network typically involved in cognitive motor functions was detected (red e yellow), while a decrease in other areas including the angular gyrus and the anterior temporal lobes (blue) was found. In healthy controls activity was slightly lateralized to the left hemisphere. In patients, left hemisphere activity was reduced due to the ischemic defects (3B left column). For the contrast control > stroke overall activation levels were greater in controls in both hemispheres (3B right column).

activation in the left MFG/SFS/dorsal IFG and in the right IFG, pars orbitalis (Fig. 4B). Impairment of tool selection and tool execution was rare and only found in combination with other deficits. Whether those findings are an effect of a “networkspecific” incomplete compensation or express dysfunctional activation resulting in behavioral abnormalities cannot be deduced from these data. To assure, that activation was not determined by the control paradigm (Circle), all analyses were repeated for the Condition ‘Circle’, which did not yield any significant results (Supplementary Figure S3). The significant difference of time from stroke onset to fMRI between the group of patients with and without limb apraxia did not influence the results (see control analysis, covarying the variable stroke onset-fMRIdelay, Supplementary Figure S4).

3.5.

Effect of lesion location on activation pattern

In a 2
2 between subject factorial design (ANOVA) the interaction effects of the factors lesion location (frontal vs parietal)
test performance (deficit vs no deficit) were assessed (see Table 2 for an overview over demographical and behavioral data of the 4 groups).

Overlaps for the frontal (n ¼ 12) and the parietal (n ¼ 12) lesion cluster after matching for age and lesion volume are shown in Fig. 5A. The group of frontal strokes shows a cortical lesion overlap in the interior frontal gyrus, pars opercularis and the adjacent precentral gyrus (55/6/12) and a second maximum in the subcortical white matter, while the maximum lesion overlap in the parietal region was found in the IPL (44/40/24). A significant interaction effect (p < .001 uncorrected) was determined in the left anterior middle temporal gyrus (MTG) (Fig. 5B): patients with frontal lesions and deficient performance of tool-associated actions showed higher activation levels elicited by the action observation paradigm compared to the group of parietal lesions or to non-apraxic patients. Similar results were obtained for computing interaction effects in the analyses for the subscores pantomime and imitation (Fig. 5C). A multiple regression analysis using 31 patients (n ¼ 16 with frontal and n ¼ 15 with parietal stroke) for the subscores pantomime and imitation of object use, controlling for age and lesion size. The main effects of the 2
2 ANOVA are reported in the Supplement (Supplementary Figure S5), control analyses for the condition ‘Circle’ are depicted in Supplements

c o r t e x 1 2 0 ( 2 0 1 9 ) 1 e2 1

11

Fig. 4 e Group comparison of functional MRI activity related to behavioral performance. Surface renderings show results of the statistical group comparisons on brain activity elicited by the fMRI task. Higher activation levels for patients with impaired performance in overall object-associated tasks (A) were found in the bilateral posterior middle temporal gyrus (pMTG), in the anterior cingulate cortex (ACC), ipsilesional in the middle frontal gyrus (MFG) and superior frontal sulcus (SFS) as well as contralesionally in the inferior frontal gyrus/sulcus and precentral gyrus. In the left hemisphere for peak voxels, no patients presented with a lesion to the frontal activity clusters and only two patients with lesion to the pMTG. The analysis with binarized subscores (b) revealed a separation of activity with an activation of bilateral pMTG in patients with deficient pantomime and activity in the left MFG and right inferior frontal gyrus (IFG) for deficient imitation of object use. Activation levels for the group of stroke patients with and without deficit were extracted form peak clusters with a sphere of 6 mm, * indicates a mean activity level different from zero (t-test against zero, p < .01). Color bar indicates T-values, depicted for the significance threshold of p < .001 uncorrected. Anterior cingulate cortex (ACC); superior frontal sulcus (SFS); middle frontal gyrus (MFG); posterior middle temporal gyrus (pMTG); inferior frontal gyrus (IFG).

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Table 2 e Overview of demographical and behavioral measures of the four groups in the 2 £ 2 ANOVA. Frontal NoDef n ¼ 4 Age (years) Mean 63.50 SD 15.26 Min 43.00 Max 77.00 Gender male/female 3/1 Lesion size (cm3) Mean 14.71 SD 10.42 Min 2.70 Max 27.31 NIHSS on admission Mean 3.75 SD 1.26 Min 2.00 Max 5.00 NIHSS on discharge Mean 1.50 SD .58 Min 1.00 Max 2.00 Pantomime of object use Mean 13.00 SD 1.41 Min 11.00 Max 14.00 Imitation of object use Mean 9.00 SD 1.15 Min 8.00 Max 10.00 Tool execution Mean 10.00 SD .00 Min 10.00 Max 10.00 Tool selection Mean 9.75 SD .50 Min 9.00 Max 10.00 Birmingham object recognition subtest 11 Mean 31.25 SD .96 Min 30.00 Max 32.00 Corsi block-tapping test forward Mean 4.75 SD 1.50 Min 4.00 Max 7.00 Corsi block-tapping test reverse Mean 5.25 SD .50 Min 5.00 Max 6.00 Token test points Mean 38.75 SD 21.84 Min 6.00 Max 50.00

Parietal

Kruskal Wallis

Def n ¼ 7

NoDef n ¼ 4

Def n ¼ 7

67.71 11.01 46.00 79.00 6/1

64.25 13.50 45.00 75.00 4/0

71.86 5.79 61.00 78.00 3/4

1.29 (p ¼ .73)

33.21 22.26 1.63 61.56

15.19 13.65 1.81 33.65

29.96 15.83 4.23 48.06

3.97 (p ¼ .26)

11.71 6.60 3.00 22.00

7.25 4.11 2.00 11.00

5.14 4.06 1.00 11.00

5.83 (p ¼ .12)

4.14 2.34 2.00 9.00

.75 .50 .00 1.00

2.00 2.45 .00 7.00

10.55 (p ¼ .01)

5.57 4.39 .00 12.00

12.25 1.50 11.00 14.00

8.29 4.57 .00 13.00

11.69 (p ¼ .01)

6.14 2.61 1.00 9.00

9.50 1.00 8.00 10.00

6.57 2.51 3.00 10.00

3.36 (p ¼ .30)

8.57 2.94 2.00 10.00

10.00 .00 10.00 10.00

8.14 1.68 6.00 10.00

7.88 (p ¼ .05)

5.57 2.94 2.00 10.00

10.00 .00 10.00 10.00

7.57 2.64 2.00 10.00

11.74 (p ¼ .01)

30.43 1.90 27.00 32.00

31.50 .58 31.00 32.00

31.57 .79 30.00 32.00

1.93 (p ¼ .59)

4.29 1.25 3.00 6.00

4.75 1.26 3.00 6.00

5.14 1.35 3.00 7.00

1.50 (p ¼ .68)

3.00 2.24 .00 6.00

4.50 1.00 4.00 6.00

4.00 1.15 2.00 5.00

5.99 (p ¼ .11)

38.33 12.61 20.00 50.00

50.00 .00 50.00 50.00

31.00 23.59 .00 50.00

3.93 (p ¼ .27)

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13

Fig. 5 e Interaction effect of lesion cluster £ behavioral performance. A depicts lesion overlaps of frontal (n ¼ 12) and parietal (n ¼ 12) lesions. In a 2 £ 2 ANOVA (Lesion cluster £ behavior) a significant interaction effect was detected in the left anterior middle temporal gyrus (anterior MTG), patients with frontal lesions and behavioral deficit show higher activation levels (B) as well as for the subscores pantomime and imitation of object-associated gestures (C). For each activation cluster in the left hemisphere a multiplanar image is presented, to precisely locate the activation cluster (right column). Furthermore, activation levels for the peak voxel of significant interaction effects are depicted: red lines indicate activation levels in frontal and blue lines indicate activation levels in parietal lesions. Color bar indicates F-values, depicted for the significance threshold of p < .001 uncorrected. Anterior temporal lobe (ATL); middle temporal gyrus (MTG), frontal (Fro), parietal (Par).

(Supplementary Figure S3). Again, matching for age and lesion size and covarying time between stroke onset and fMRI (Supplementary Figure S4) allowed for controlling the results for possible confounding factors.

Further, a multiple regression analysis with interaction effects in the subsample of patients with frontal and parietal lesions (n ¼ 31 with n ¼ 16 frontal and n ¼ 15 parietal) without initial matching was computed and included behavioral

14

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performance as repressors for the variable of interest (pantomime of object use, imitation of object use), the dichotomous variable lesion location. The results revealed a significant two-way interaction on bold signal change for lesion location
behavioral performance in the bilateral anterior MTG for pantomime (left anterior temporal lobe e ATL (t ¼ 5.28, p < .001), right ATL (t ¼ 5.11, p < .001)) and in the left anterior MTG for imitation of object use (t ¼ 7.82, p < .001). Comparable with the factorial analysis (Fig. 5), patients with frontal lesion and impaired behavioral performance show higher activity levels compared to patients with parietal lesions and impaired behavioral performance in the anterior MTG (Fig. 6), which underlines the modulating effect of lesion location on brain activation in relation to a deficit. For imitation of object use, patients with frontal lesion and deficit even reveal a positive-going bold-contrast in comparison to the other groups (Fig. 6B, Supplement Table S5). The analysis was controlled for age and lesion size.

4.

Discussion

This study clearly illustrates the entanglement of post-stroke network anatomy and its affection by the lesion site, the patients' behavioral performance and the brain's functional activation status. By studying all of these three components simultaneously for the first time in our study, we here present results broadening the understanding of network-based higher cognitive functions and their impairment after stroke (Fig. 1). We were able to detect deficit-specific early network activity changes in within key areas of the action observation network and domain general areas (pMTG, MFG/SFS), regardless of the lesion location. Our data further affirm an interaction effect between the lesion site and behavioral performance and suggest a modulation of activity between the

known processing streams, which is determined by the anatomy of the lesion.

4.1. Acute post-stroke apraxia is associated with altered network-state Our results support the hypothesis that post-stroke apraxia incorporates network effects and might result from a dysfunction of the network supporting praxis. First, as a correlate of acute network dysfunction in the acute phase after stroke, overall activity of stroke patients with and without apraxia was reduced. This finding is in line with previous studies on higher cognitive functions (language, spatial attention) in stroke patients, which detected a network dysfunction in the acute stage (Saur et al., 2006; Umarova et al., 2016). Secondly, within the globally downregulated network, apraxic compared to non-apraxic patients showed specific activation patterns, within pMTG and MFG/SFS (Fig. 4). Structurally, the descriptive lesion overlaps did not yield a difference in lesion distribution between apraxic and nonapraxic patients. Voxel-based lesion symptom mapping revealed IPL significantly correlated with apraxia, a finding, which corroborates previous studies on apraxia in tool-related actions (Buxbaum et al., 2014; ; Goldenberg & Spatt, 2009; Hoeren et al., 2014), but is not exclusive to impaired toolrelated actions, as frontal and temporal lesions also induce tool-related deficits (see the Introduction for a more detailed description, also regarding the heterogeneity of lesion correlates in tool-related tasks). In the light of the heterogeneity in lesion studies, our data offer a new perspective on action cognition. Identifying activity patterns, related to the presence of e.g., deficits in pantomiming or imitation object use (Fig. 4), we conclude that apraxic deficits result from a deficit-related dysfunctional state of the network. For example, different

Fig. 6 e Multiple regression with interaction effect of lesion cluster £ behavioral performance. Axial, coronary and transversal slices show voxels where the two-way interaction between lesion location and behavioral performance reached significance (A for pantomime of object use and B for imitation of object use). Regression lines for clusters indicate how bold signal (y-axis) changes in dependence on behavioral performance and lesion location (frontal ¼ red, parietal ¼ blue), behavioral performance is depicted as mean, mean e SD and mean þSD, reflecting more or less impaired patients, respectively. Color bar indicates T-values, depicted for the significance threshold of p < .001 uncorrected. Middle temporal gyrus (MTG), frontal (Fro), parietal (Par).

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lesion locations (e.g., IPL lesions or IFG lesions) can be related to pantomime deficits, as they functionally induce a comparable disturbance of information processing within the network e.g., within the ventro-dorsal stream, relevant for processing movement-related aspects of object use. Lesions, affecting an important hub within a network and induce a behavioral deficit can be referred to as critical lesions (Gratton, rez, & D'Esposito, 2012; Rijntjes & Weiller, 2002). Nomura, Pe The effect of network dysfunction due to focal and functionally relevant damage is also discussed in the context of diaschisis, describing the dysfunction of cortical brain regions that are remote but functionally connected to the lesion (Price, Warburton, Moore, Frackowiak, & Friston, 2001; Sare, 2016; Weiller, Vry, Saur, Umarova, & Rijntjes, 2015). Network effects also have been evaluated as a reason for the considerable variability in lesion studies (Gajardo-Vidal et al., 2018). In accord with our results, studies on other neuropsychological domains identified distributed network effects with an upregulation of remote and contra-lesional areas during the early stage of aphasia (Saur, et al., 2006) and mechanisms of acute network dysfunction in spatial attention deficits (Catani et al., 2012; Mesulam, 1990). At this point our data do not allow for a definite conclusion on whether the increased activity represents an incomplete compensatory mechanism (i.e., beneficial) or, alternatively, is related to poor performance.

4.2. Deficit-specific activation patterns relate to functional relevant areas for praxis and might reflect early adaptive network changes Deficit-specific modulation of brain activation occurs in regions, related to action observation, cognitive motor function and cognitive control. Different apraxic deficits exert distinct activation patterns (Fig. 4). Relative upregulation of activation in non-lesioned functional relevant areas might reflect higher demands during action observation in behaviorally impaired patients. This further corroborates the understanding of the functional architecture of the praxis network and reaffirms the hypothesis of early network-based reorganization. Presence of limb apraxia was related to increased activation of posterior MTG. Posterior MTG is part of the ventral stream for praxis and has been suggested to be a key node for semantic tool-use information, action knowledge and pantomime (Binkofski & Buxbaum, 2013; Buxbaum et al., 2014; nine, 2010; Hoeren et al., 2014; Ishibashi, Buxbaum & Kale nine et al., 2010; Pobric, Saito, & Lambon Ralph, 2016; Kale Martin et al., 2017; Watson & Buxbaum, 2015), and to play a role in the integration of visuomotor information and analysis of biological motion (Beauchamp, Lee, Argall, & Martin, 2004) as well as in the network of semantic control (Corbett, Jefferies, Ehsan, & Ralph, 2009; Hickok & Poeppel, 2007; Jefferies & Lambon Ralph, 2006). pMTG activation in apraxic patients can be interpreted as correlate for increased effort in visual analysis videos and retrieval of additional semantic information during action observation. The activation of pMTG is especially relevant in patients with deficits in pantomime of tool use, which relies on processing of communicative (Finkel et al., 2018) and semantic information in the ventral stream (Hoeren et al., 2014; Niessen et al., 2014; Vry et al., 2015). Patients with a lesion, resulting in pantomime

15

deficits, increase activation in pMTG, potentially as a compensatory effort. Differential activation in dependence on the presence or absence of limb apraxia in the frontal lobe is located in regions, where the ventro-dorsal stream (dorsal IFG), parts of the mirror neuron system (Buccino, Binkofski, & Riggio, 2004; Hamzei et al., 2016; Rizzolatti & Matelli, 2003), and the multiple demand networks (MFG/SFS) (Fedorenko, Duncan, & Kanwisher, 2013) converge. Functionally, these region facilitate imitative capacities and action observation (Buccino et al., 2004; Caspers, Zilles, Laird, & Eickhoff, 2010; Hamzei et al., 2016). Given the fact that fMRI analysis only reveals differential levels of activity, we cannot determine, which level of activity is functional in relation to the task. Overall, apraxic patients might show higher levels of activation in MFG/IFS compared to non-apraxic patients to process the visuomotor information in the passive task. Patients with deficits in the imitation of object-associated actions, which relies on the ventro-dorsal stream (Dressing et al., 2018) and on the capacity to integrate visuomotor information (Bonivento, Rothstein, Humphreys, & Chechlacz, 2014; Chaminade et al., 2005), show this pattern in isolation and even show an upregulation of left MFG during the task, thus might apply a different strategy compared to patients with pantomime deficits. As part of the multiple demand network, which is frequently recruited during executively-demanding tasks across domains, and is not closely tied to specific cognitive demands (Fedorenko et al., 2013), reduced deactivation or even activation in left MFG/IFS and right IFG might be functional and might contribute to information processing and cognitive control during the task. ACC can also be attributed to the network for cognitive control. Mechanisms of cognitive control have been reported to support recovery e.g., in post stroke aphasia (Brownsett et al., 2014; Geranmayeh et al., 2017). Conversely e assuming that activity levels in nonapraxic patients are functional e reduced deactivation could correlate to a dysfunctional state of bilateral MFG/IFS, interfering with a successful information processing in the action cognition network (Anticevic et al., 2012; Binder, 2012; Gusnard, Raichle, & Raichle, 2001; Laird et al., 2009). Right hemisphere homologues were activated in all groups of apraxic patients compared to non-apraxic patients. Contralesional activity might facilitate recovery (Hartwigsen et al., 2013; Leff et al., 2002; Martin et al., 2016; Musso et al., 1999; Saur & Hartwigsen, 2012; Umarova et al., 2016; van Oers et al., 2010; Weiller et al., 1992, 1995). pMTG activation in particular might be supportive for pantomime, a finding, which is supported by a recent study in dementia patients, where grey matter thickness in the right temporal lobe was positively correlated to pantomime capacities (Johnen et al., 2016). Contrarily however, a disinhibition or maladaptive influence of activation in the contra-lesional hemisphere has been reported (Heiss & Thiel, 2006; Naeser et al., 2004; Rosen et al., 2000; Thiel et al., 2006).

4.3. Lesion location modulates ventral stream activity associated with apraxia Activity changes observed in apraxic patients are further modulated by the location of the lesion (frontal vs parietal)

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(Figs. 5 and 6). Patients with a lesion to the IFG, a key node in the praxis network, and a behavioral deficit in tool-related tasks showed higher levels of bold signal in the bilateral anterior MTG and in the ATL. ATL is an amodal hub that supports semantic cognition by linking distributed modalityselective regions and is a key region to the ventral stream in language and praxis (Jackson, Hoffman, Pobric, & Lambon Ralph, 2016; Jefferies, 2013; Lambon Ralph, 2013; Martin,  n et al., 2015; Visser, Jefferies, Embleton, & et al., 2016; Sanjua Lambon Ralph, 2012; Warren, Crinion, Lambon Ralph, & Wise, 2009). ATL is connected to the IPL via the middle longitudinal fascicle (mdLF) and to the anterior IFG (mainly BA 47) via the uncinate fascicle (Makris & Pandya, 2009; Makris et al., 2013, 2017; Schmahmann et al., 2007). Differentially higher activity levels in the anterior MTG after frontal lesions in apraxic patients might represent a remote network effect in acute apraxia. In our study, on the one hand, ATL activation could occur due to missing inhibitory signals from the frontal lobe (frontal disinhibition). In healthy control subjects, activity in the ATL, as part of the default mode network, is downregulated during action observation in a task-dependent manner (Anticevic et al., 2012) (Fig. 3). Thus, lesions to the IFG could impair this hypoactivation during the task in the ATL and by this also compromise performance. Yet, considering the location of the lesion cluster in the IFG, which spares the anterior parts of IFG, the connection between anterior IFG and ATL should be intact in our cohort of patients. On the other hand, IPL and ATL are connected via the mdLF, and several authors hypothesized that information from the multimodal association cortex in the inferior parietal and temporal lobe are transported via the mdLF towards the ATL (Makris et al., 2013, 2017). ATL activation could therefore be an effect of early compensation and facilitate semantic understanding of the observed videos, as ATL is regarded a part of the ventral stream (‘understanding’; cognitive/semantic route) (Saur et al., 2008; Vry et al., 2015; Weiller et al., 2009, 2011). As this activation was not registered in patients with deficit and parietal lesions, the ventral stream potentially can be used for early compensation only when the feeding components of the network (parietal cortex and mdLF) are intact. A similar effect has recently been shown in the domain of language, where damage to IFG has been associated with an upregulation of activity in connected areas with similar function (e.g., pMTG and ATL) in semantic tasks (Hallam et al., 2018) or in a lesionactivity mapping study, where modulation of activity in the temporal lobe was related to parietal lesions (Garcea et al., 2018). Corresponding to the current study, an investigation in chronic stroke patients with large infarcts showed that better behavioral performance in tool-related tasks was supported by activity in the putative healthy network and in additional areas (Martin et al., 2016). In particular, right IFG, left pMTG as well as temporal lobe activation were related to intact tool-related performance e similarly, we find activity in those regions correlated with impaired performance in the acute stage. This supports the view of early network-based reorganization. Although the question, if the differential levels of activation in the temporal lobe in the interaction analysis are functional or dysfunctional, remains partly unanswered, taken together, in our study for the first time a lesion- and

deficit-dependent modulation of network activation was detected. This indicates an interaction between the dorsal and the ventral parts of the network after acute stroke and underlines the importance to investigate network function and dysfunction in relation to the stroke lesion.

4.4.

Limitations

Our approach to study functional network activation in dependence on the behavioral deficit and the lesion location in acute stroke patients implies limitations. First, methodologically, examining acute patients is limited by the patients' tolerance to extensive behavioral testing and functional imaging in the first days after stroke. For this reason, we chose a passive fMRI paradigm, which activates the network of interest independently from the behavioral performance (Martin, Nitschke, et al., 2016). However, given contradictory data on the congruency of the action observation and the action execution network (Buxbaum & Saffran, 2002; Garcea, Dombovy, & Mahon, 2013; Negri et al., 2007; Rumiati, Zanini, Vorano, & Shallice, 2001), we cannot rule out that regions of the action cognition network are activated to a different extent while observing or executing actions. This problem could be overcome by using an active tool-use paradigm, which, in turn would encompasses a different set of limitation (e.g., movement artifacts). Secondly, we put great effort in selection of the patients of the group with frontal and parietal stroke and into controlling our analyses for potential confounding factors. This resulted in a small group size, despite an initially comparatively large sample of patients. However, it allows us to obtain highly specific results in network activation, which integrate behavioral and anatomical information. Thirdly, the question concerning the functional relevance of neural correlates of acute apraxia and the activity modulated by lesion location and behavior remains partly unanswered, as we conducted the analyses only in the acute stage and as the baseline condition itself modulates network activation and therefore might influence activation levels. To further elucidate the functional meaning of the early alterations in network activation, e.g., longitudinal analyses are needed. As described above, apraxia is understood as a network disorder and our results are interpreted in this regard. A network disorder may only be incompletely described by passively elicited alteration of functional activation in taskspecific or domain-general areas. Therefore, functional interaction (e.g., in resting state analyses) between those regions might further elucidate the meaning of the functional activation and improve the understanding of neural correlates of behavioral impairment and recovery.

5.

Conclusion

We investigated functional activation of the praxis network after acute stroke, with a novel approach that combines structural lesion data, behavioral offline-performance and passive functional activation of the network-of-interest. Network effects in acute apraxia were shown to consist of altered activity levels in remote, non-lesioned areas of the network and in domain-general areas in patients with

c o r t e x 1 2 0 ( 2 0 1 9 ) 1 e2 1

impaired behavioral performance. Those changes in functional brain activation related to impaired tool-manipulation broaden our knowledge about the functional brain status in patients with limb apraxia and corroborate data from structural lesion studies. As areas of altered activation are involved in domain-specific cognitive motor function, cognitive control and support semantic retrieval, they might be feasible candidates for early functional compensation. Our study offers the first evidence, that the location of lesion exerts an additional effect on network response in acute apraxia. Differential activation for frontal and parietal lesions in the bilateral middle and ATL in patients with impaired offline-performance suggests an interaction between the dorsal and the ventral parts of the network after impairment of the network for motor cognition due to acute stroke. The study determined deficit-related activation patterns, which might detect early mechanism of network reorganization after acute left-hemispheric stroke in apraxia. Furthermore, it underlines the necessity to combine structural lesion information, behavioral parameters and functional activation to comprehensively examine those mechanisms in acute stroke patients.

Conflict of interest The authors declare no conflict of interests.

CRediT authorship contribution statement Andrea Dressing: Conceptualization, Data curation, Formal analysis, Funding acquisition, Investigation, Methodology, Project administration, Visualization, Writing - original draft, Writing - review & editing. Christoph P. Kaller: Conceptualization, Formal analysis, Methodology, Project administration, Visualization, Writing - original draft, Writing - review & editing. Kai Nitschke: Formal analysis, Methodology. LenaAlexandra Beume: Data curation, Investigation. Dorothee Kuemmerer: Data curation, Investigation. Charlotte S.M. Schmidt: Data curation, Investigation. Tobias Bormann: Conceptualization, Writing - review & editing. Roza M. Umarova: Conceptualization, Investigation. Michel Rijntjes: Conceptualization, Writing - original draft, Writing - review & editing. Cornelius Weiller: Conceptualization, Funding acquisition, Project administration, Writing - original draft, Writing - review & editing. Markus Martin: Conceptualization, Funding acquisition, Methodology, Project administration, Writing - original draft, Writing - review & editing.

Acknowledgments and funding This work was supported by the BrainLinks-BrainTools Cluster of Excellence funded by the German Research Foundation (DFG, grant #EXC1086). We thank the occupational therapists for conducting the neuropsychological testing; without their careful examinations, this study would not have been

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possible, and H. Mast for assistance in data acquisition. We thank K. Whittaker for proofreading the manuscript.

Supplementary data Supplementary data to this article can be found online at https://doi.org/10.1016/j.cortex.2019.05.005.

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