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. Author manuscript; available in PMC: 2022 Sep 1.
Published in final edited form as: Cortex. 2021 Jun 24;142:272–282. doi: 10.1016/j.cortex.2021.05.022

Aberrant Prefrontal Cortical-Striatal Functional Connectivity in Children with Primary Complex Motor Stereotypies

Farhan Augustine a,c, Mary Beth Nebel a,b, Stewart H Mostofsky a,b, Mark E Mahone b, Harvey Singer b
PMCID: PMC8405561  NIHMSID: NIHMS1718479  PMID: 34303880

Abstract

Motor stereotypies are rhythmic, repetitive, prolonged, predictable, and purposeless movements that stop with distraction. Although once believed to occur only in children with neurodevelopmental disorders such as autism, the presence and persistence of complex motor stereotypies (CMS) in otherwise typically developing children (primary CMS) has been well-established. Little, however, is known about the underlying pathophysiology of these unwanted actions. The aim of the present study was to use resting-state functional magnetic resonance imaging to evaluate functional connectivity within frontal-striatal circuits that are essential for goal-directed and habitual activity in children with primary complex motor stereotypies. Functional connectivity between prefrontal cortical and striatal regions, considered essential for developing goal-directed behaviors, was reduced in children with primary CMS compared to their typically developing peers. In contrast, functional connectivity between motor/premotor and striatal regions, critical for developing and regulating habitual behaviors, did not differ between groups. This documented alteration of prefrontal to striatal connectivity could provide the underlying mechanism for the presence and persistence of complex motor stereotypies in otherwise developmentally normal children.

Keywords: Primary complex motor stereotypies, functional connectivity, frontal-striatal circuitry, habitual behavioral pathways, goal-directed behavioral pathways

1. INTRODUCTION:

Motor stereotypies are rhythmic, repetitive, prolonged, predictable (in form, amplitude, body parts involved), purposeless movements that stop with distraction (Singer, 2011). Children with persistent stereotypes are separated into two major clinical categories: ‘primary’ for those who are otherwise developmentally normal and a ‘secondary’ category for those with a neurodevelopmental or mental disorder, are medication-induced, or associated with other neurological conditions, e.g., autism spectrum disorder (ASD), Rett syndrome, a paraneoplastic or neurodegenerative disorder, inborn error of metabolism, or other conditions (Martino & Hedderly, 2019). Precise estimates of prevalence of stereotypic movements in typically developing children are unknown; postulated prevalence of 2–4% (Foster, 1998).

Primary motor stereotypies have been further subdivided into three groups:common (e.g., rocking, head banging, finger biting, pencil drumming, leg swinging); head nodding; and complex motor (bilateral hand and arm flapping/waving, finger wiggling in in front of the face. Common stereotypies occur intermittently in typically developing (TD) children, during infancy/toddlerhood, and often resolve in preschool years (Thelen, 1981). In contrast, primary complex motor stereotypies (CMS) typically begin before the age of 3 years (Harris et al 2008) and persist through at least adolescence (Oakley et al., 2015). Movements are exacerbated by excitement, fatigue, stress, or boredom, have a duration of seconds to minutes, and occur multiple times per day. Although some children with primary complex stereotypies report enjoyment from performing the movement or are mentally replaying a prior activity or television program, most are unaware of their presence (for a review, see (K. M. Harris et al., 2008a; Singer et al., 2015)). Primary CMS have been successfully treated with behavioral therapy (Singer et al., 2018; Specht et al., 2017)

Although the exact pathophysiology of primary CMS is unknown, movements are believed to be habitual (involuntary) behaviors, based on their repetitive, fixed, stimulus-responsive, and reinforcement-driven manner that lacks an apparent goal (Colwill & Rescorla, 1990; Graybiel, 2008). Additional clinical evidence supporting stereotypies being an involuntary behavior include their repetitive, persistent nature, exacerbation when engrossed in activities, lack of a clear benefit, nonexistent premonitory urge, and the potential for movements to cause either psychosocial or physical difficulties. Further, an electrophysiological study in children with primary CMS has shown that complex stereotypies are not preceded by premotor movement-related cortical (Bereitschaft) potentials, which suggests that stereotypies utilize pathways that differ from those involved in voluntary movements (Houdayer et al., 2014). In terms of potential circuits, animal and human studies have identified two distinct cortical-striatal pathways that are involved in goal-directed and habitual behavioral activities (Balleine & O’Doherty, 2010; de Wit et al., 2012). More specifically, it has been speculated that the abnormality in CMS could reside within either the habitual system (premotor cortex (PMC)/supplementary motor cortex (SMC)-striatum) that relies on reflexive, automatic elicitation of actions; or the goal-directed system (prefrontal cortex-striatum) that generates decisions based on the evaluation of the consequences of action (McNamee et al., 2015; Simmler & Ozawa, 2019; Singer, 2013; Tanaka et al., 2008). Other cortical systems, e.g., involving inhibition or or other brain regions, have also been suggested pathophysiological mechanisms(Mirabella et al., 2020). Prior neuroimaging protocols in children with primary CMS have demonstrated a significant volume reduction in the putamen (Mahone et al., 2016) and reduced GABA levels in the anterior cingulate and striatal subregions (A. D. Harris et al., 2016), compared to typically developing children. Nevertheless, the precise neuroanatomical localization for motor stereotypies remains elusive (Gao & Singer, 2013).

The goal of this study was to investigate the pathophysiology of childhood primary CMS using resting-state functional magnetic resonance imaging (rsfMRI) and a striatal seed-to-frontal voxel approach. More specifically, the inter-regional correlations of intrinsic fluctuations in blood-oxygen-level-dependent (BOLD) signals were determined. BOLD imaging is a standardized tool for evaluating the most active parts of the brain, and its efficacy in evaluating the functional connectivity of brain networks is well established (Hashemiyoon et al., 2017; Hohenfeld et al., 2018; Jiang et al., 2019). The primary focus in this study was frontal–striatal circuits, recognizing their involvement in motor activity, regulation of goal-directed and habitual behaviors, and proactive and reactive inhibition (Aron, 2011; Zandbelt & Vink, 2010). Based on data suggesting that the selection of actions in response to external and internal stimuli involves a balance between goal-directed and habitual pathways (de Wit et al., 2012), two alternative hypotheses were tested in this study. The first hypothesis was that, compared to their typically developing (TD) peers, children with primary CMS would show excessive functional connectivity among regions implicated in regulating habitual behaviors (i.e, increased functional connectivity between motor/premotor cortical regions and striatal regions). The second hypothesis was that, compared to TD children, children with primary CMS would show altered connectivity among brain regions implicated in regulating goal-directed behaviors (i.e., between prefrontal and striatal regions).

2. MATERIALS and METHODS:

The study was approved by the Johns Hopkins Medicine Institutional Review Board. Written consent was obtained from a parent/guardian, and assent was obtained from the child. 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. Legal copyright restrictions prevent public archiving of the various tests and assessments used in this study, which can be obtained from the copyright holders in the cited references. No part of the study procedures or analysis plans was preregistered in a public registry prior to the research being conducted.

2.1. Participants:

A total of 37 children (aged 8–13 years) with primary CMS completed the resting-state functional magnetic resonance imaging (rsfMRI). However, only twenty-four scans were motion free. Thus, only the data from twenty-four children with the diagnosis of primary CMS (mean age 10.36 ± 1.44), and twenty-four age-matched typically developing (TD) children (mean age 10.39 ± 1.17) were used for this study – the latter group was drawn from a larger sample of 195 TD children with usable rsfMRI data. Demographic information is provided in Table 1, along with inferential statistics regarding diagnostic group differences. Handedness was assessed using parent responses to the Edinburgh Handedness Inventory modified for children (Oldfield, 1971). Socioeconomic status was assessed using Hollinghead’s four-factor index of social status (Hollingshead, 1975). A copy of the raw data can be obtained from Open Science Framework database: https://osf.io/pba5j/ (DOI 10.17605/OSF.IO/PBA5J)

Table 1:

Sample Demographics and rsfMRI quality characteristics stratified by group.

Group Gender (F/M) Age At Scan FSIQ SES Handedness (R/Mixed/L) DuPaul Total Percentile Stereotypy Severity Scale Mean FD
CMS 3/21 9.35 (1.04) 106.5 (13.46) 56.3 (7.52) 23/1/0 56.0 (15.7) 20.9 (11.5) 0.19 (0.17)
TD 5/19 9.71 (0.88) 111.5 (13.62) 52.5 (7.80) 20/1/3 NA NA 0.15 (0.08)
P 0.45 0.21 0.23 0.13 0.19 NA NA 0.29
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The mean is listed for each variable with the standard deviation in parentheses. The bottom row indicates the p-value associated with the group comparison (t-tests for continuous variables, chi-square for categorical variables). F female, FD, framewise displacement (a measure of head movement during the resting-state scan), FSIQ Wechsler Intelligence Scale for Children Full-scale Intelligence Quotient, M Male, L left-handed, R right-handed, SES Hollingshead four-factor Index of Socioeconomic Status.

Participants with CMS were recruited from the Johns Hopkins Pediatric Movement Disorder Clinic or associated website (http://www.hopkinsmedicine.org/neuro/stereotypy) and evaluated by a child neurologist with expertise in pediatric movement disorders (HSS). The confirmation of stereotypes was made either by direct observation in clinic or via parents’ video. All participating children with CMS had symptom onset before age 3 years and persisting common core features including either bilateral flapping/waving arm and hand movements or fluttering fingers in front of the face. Other common aspects of the motor repertoire included an association with periods of engrossment, excitement, stress, fatigue or boredom, a prolonged duration, and stopping with distraction (K. M. Harris et al., 2008b; Mahone et al., 2004). Aspects of our participants’ repertoires that varied across subjects included the specific developmental sequence of movements, their duration, and presence/absence of other movements or vocalizations accompanying the core activities (e.g., head posturing, mouth opening, pacing, jumping, sounds, etc.) and all comorbidities.

The severity of stereotypy symptoms was rated by parents on the day of the MRI scan using the Stereotypy Severity Scale (SSS, (Miller et al., 2006)). The SSS is a 5-item caregiver questionnaire in which the parent rates the child’s motor stereotypies by rating the number, frequency, intensity, and interference of stereotypies (maximum, 18 points) and the global impairment (maximum, 50 points) during the past few days. In addition, since ADHD is a common comorbid problem in primary CMS, occurring in approximately one-third of children with primary CMS (Harris et al, 2008), ADHD symptom severity was assessed using parent responses to the DuPaul ADHD Rating Scale (DuPaul et al., 1998). The ADHD Rating Scale askes a caregiver to rate the severity of inattention and hyperactivity/impulsivity symptoms over the last six months and yields two subdomain scores: inattention and hyperactivity/impulsivity. Higher DuPaul scores indicate more severe symptoms.

Scans from TD children were acquired as part of an on-going study at the Center for Neurodevelopmental and Imaging Research (CNIR) at the Kennedy Krieger Institute (KKI). Participants were recruited via advertisements posted in the community, local pediatricians’ and psychologists’ offices, local schools, social service organizations, outpatient clinics at Kennedy Krieger Institute, and by word of mouth. Exclusion criteria for children in both the CMS and TD groups included a current full-scale intelligence quotient of less than 80 as measured by the Wechsler Intelligence Scale for Children version IV (WISC-IV) (Wechsler, 2003) or version 5 (WISC-V) (Wechsler, 2014); a diagnosis of autism spectrum disorder (ASD); presence of visual impairment, hearing loss ≥25 dB, or neurological disorder (e.g., epilepsy, cerebral palsy, and traumatic brain injury); treatment with psychotropic medication in the past three months; and a medical contraindication to MRI procedures. No participant had a history of a severe chronic medical disorder, diagnosed genetic disease, or psychotic disorder. Lastly, since secondary motor stereotypies are commonly observed in children with ASD, and the goal was to investigate ‘primary’ motor stereotypies, children were excluded if they had a first-degree relative with ASD or if parent responses revealed a history of a developmental or psychiatric disorder.

2.2. Resting-State fMRI acquisition:

All participants completed an initial mock scan session to decrease anxiety, acclimate to the scanning environment, and train to lie still. No sedation was used. RsfMRI scans were acquired on a Phillips 3T MRI scanner (Achieva, Philips Healthcare, Best, The Netherlands) using a single-shot, partially parallel (SENSE) gradient-recalled echo-planar sequence (parameters: TR/TE: 2,500 ms / 30 ms, flip angle = 70°, SENSE acceleration factor of 2, 3-mm axial slices with no slice gap and an in-plane resolution of 3.05 × 3.15 mm (84 × 81 voxels), 156-time points/6.5 minutes) and either an 8-channel or a 32-channel head coil. An ascending slice order was used, and the first four volumes (10 s) were discarded at acquisition to allow for magnetization stabilization. Children were instructed to relax and focus on a crosshair while remaining as still as possible.

2.3. rsfMRI processing:

Data were processed using Statistical Parametric Mapping software (SPM12, Wellcome Trust Centre for Neuroimaging, Department of Cognitive Neurology, Cambridge, UK) and custom code written in Matlab (Mathworks, Inc.) (custom code is available on GitHub via the following link- https://github.com/KKI-CNIR/CNIR-fmri_preproc_toolbox). RsfMRI scans were slice time adjusted, rigid body realigned to account for head motion, and normalized to the Montreal Neurological Institute (MNI) template. Each rsfMRI scan was temporally detrended on a voxel-wise basis. The aCompCor strategy was used to estimate spatially coherent noise components from tissues not expected to exhibit BOLD signals; a methodology shown to selectively attenuate physiological noise and motion artifact (Behzadi et al., 2007; Muschelli et al., 2014). Recent studies have demonstrated that using a modular sequence of processing steps to minimize nuisance effects can reintroduce artifacts that earlier stages in the sequence attempted to remove (Lindquist et al., 2019). Hence, CompCor components were regressed from the data along with the realignment estimates, linear trends, and temporal bandpass regressors (.01–.1 Hz) in a unified model using 3dTproject from AFNI (Analysis of Functional Neuroimages: http://afni.nimh.nih.gov; NIMH Scientific and Statistical Computing Core, Bethesda, Maryland) to avoid reintroducing motion artifacts after nuisance regression. RsfMRI data were then spatially smoothed (6-mm FWHM).

2.4. Functional connectivity estimation:

A three-region parcellation of the striatum was utilized to examine frontal-striatal functional connectivity, as defined by the Oxford-GSK-Imanova segmentation (Tziortzi et al., 2014); available in the FMRIB Software Library (FSL) and derived from probabilistic tractography. Further, following the Tziortzi et al (2014) protocol, the frontal lobe was divided into four anatomical regions of interest (ROIs) known to be more associated with a particular function (e.g., limbic, executive, rostral motor, and dorsal motor) and then classified according to the cortical region that had the highest probability of connection with that striatal voxel. Figure 1 illustrates the resulting limbic, executive, and sensorimotor subdivisions (with sensorimotor grouping together areas of the striatum with connections to both rostral and caudal motor/premotor cortex). An ROI-to-voxel analysis was conducted to assess whether functional connectivity between each of these striatal subdivisions and the frontal lobe was disrupted in children with CMS compared to TD children. For each subject, average time-series for the three bilateral striatal ROIs were extracted from the spatially unsmoothed functional data to minimize partial-volumizing effects among the relatively small functional subdivisions of the striatum. A pairwise Pearson correlation was calculated between each average striatal ROI time-series and the time-series of every voxel within the frontal lobe in the spatially smoothed functional data. Fisher’s z-transform was applied to the correlations. Frontal-striatal functional connectivity scores further from zero reflect robust functional connectivity regardless of the sign; positive scores reflect positive correlations or functionally integrated intrinsic activity, while negative scores reflect negative/anti-correlations or functionally segregated intrinsic activity.

Figure 1: Three bilateral striatal regions of interest based on the Oxford-GSK-Imanova Connectivity Atlas.

Figure 1:

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The figure 1 illustrates the limbic, executive, and sensorimotor subdivisions. Note Sensorimotor subdivision groups together some of the striatum areas connected to both ros tral and caudal motor/premotor cortex.

2.5. Statistical Analyses

The selection of subjects in the TD group was balanced to match the motor stereotypy group based on demographic information (age, gender, handedness, intellectual ability derived from WISC-IV/WISC-5), socioeconomic status, and rsfMRI data quality covariates (mean FD). Due to a large number of covariates, the MatchIt package in R was used to calculate a propensity score for each subject to minimize selection bias and identify 24 CMS–TD pairs with the smallest distance between them (Stuart et al., 2011; Stuart & Lalongo, 2010). Functional connectivity for each striatal ROI was compared between the balanced groups using a multiple regression model. Voxelwise group comparisons were thresholded at a level of p<.001 and then cluster-corrected using 3dclustsim to an alpha level of p<.05. The cluster-level threshold was then multiple-comparisons corrected for the three seeds (i.e., p<.05/3 = 1.67*10−2).

Although study groups were initially balanced by age, the potential effect of participant’s age on frontal-striatal functional connectivity within the groups was also evaluated. More specifically, a linear regression model was developed to further assess the interaction between age and diagnosis on functional connectivity between each frontal-striatal pair that showed a significant group difference. P-values associated with the interaction terms were multiple-comparisons corrected for the number of pairs (i.e., p<.05/5 = .01).

To evaluate whether frontal-striatal functional connectivity within the CMS group was related to stereotypy severity, a linear regression analysis was performed with the SSS-total score as the outcome and functional connectivity between significantly different (CMS vs TD) frontal-striatal pairs as the predictors, controlling for age.

3. RESULTS:

3.1. Group comparisons for frontal-striatal functional connectivity

Figure 2 illustrates group distributions of functional connectivity scores for all regions showing significant group differences (p<1.67*10−2), and Table 2 contains MNI coordinates for the peaks of all significant clusters. Results show that compared to their typically developing peers, children with CMS demonstrated a pattern of reduced frontal-striatal functional connectivity specific to the executive and limbic subdivisions of the striatum. For the executive striatum, significant clusters of reduced functional connectivity were observed in both the orbitofrontal cortex (OFC) and the dorsolateral prefrontal cortex (dlPFC, Figure 3). For the limbic striatum, significant clusters of reduced functional connectivity were again observed in the OFC and dlPFC, as well as in the medial PFC (mPFC, Figure 4). No significant group differences were observed in functional connectivity between the subdivision of the striatum more associated with motor function and any frontal lobe regions. We also failed to observe any frontal regions for which children with CMS showed increased functional connectivity compared to TD children.

Figure 2: Significant group differences in frontal-striatal functional connectivity.

Figure 2:

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Split violin plots illustrating group distribution of the intrinsic synchronization of frontal and striatal regions showing significant group differences. Typically developing (TD) children are shown on the violin’s left side; children with complex motor stereotypies (CMS) are shown on the right side of the violin in grey. Horizontal lines indicate median connectivity values for each group.

Abbreviations: Dorsolateral prefrontal cortex (dlPFC), Executive Striatum (Exec Striatum), medial prefrontal cortex (mPFC), Orbitofrontal cortex (OFC).

Table 2:

Significant Functional Connectivity Differences Between Children with Stereotypies and Typically Developing Controls.

Seed Region Cluster Region MNI Coordinates (Peak) Z k
X Y Z
Executive Striatum R/L OFC 6 44 −20 −5.4 415
L dlPFC −46 26 44 −5.4 248
R dlPFC 40 28 44 −4.5 232
R dlPFC 52 38 24 −5.3 124
R dlPFC 38 50 16 −4.1 97
L dlPFC −42 48 16 −4.8 95
L OFC −30 32 −20 −4.7 89
Limbic Striatum R OFC 8 58 −14 −4.8 231
L OFC −22 54 −12 −5.4 123
R mPFC 2 62 −2 −5.1 118
L dlPFC −48 22 38 −4.6 72
R dlPFC 34 56 18 −4.4 63
R dlPFC 52 38 24 −4.5 58
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Montreal Neurological Institute (MNI) coordinates for all significant cluster regions, and seed regions are shown in this table. Abbreviations: Dorsolateral prefrontal cortex (dlPFC), Cluster size (k), Left hemisphere (L), medial prefrontal cortex (mPFC), Orbitofrontal cortex (OFC), Right hemisphere (R)

Figure 3: Significantly reduced frontal-striatal functional connectivity in CMS.

Figure 3:

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This figure shows frontal regions (in blue) with significantly reduced functional connectivity with the executive striatum in children with complex motor stereotypies. Voxelwise group comparisons were thresholded at a level of p<.001 and then cluster-corrected using 3dclustsim to an alpha level of p<.05. The cluster-level threshold was then multiple-comparisons corrected for the three seeds (i.e., p<.05/3 = 1.67*10−2). Anatomically defined delineations from the Ranta Frontal Lobe Atlas are shown for reference: the orbitofrontal cortex is shown in yellow and dorsolateral prefrontal cortex is shown in red and blue. Montreal Neurological Institute (MNI) coordinates are shown above each slice.

Figure 4: Significantly reduced prefrontal-limbic striatal functional connectivity in CMS.

Figure 4:

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The figure shows significantly reduced functional connectivity between prefrontal-limbic striatum in children with complex motor stereotypies and typically developing controls (in blue). Voxelwise group comparisons were thresholded at a level of p<.001 and then cluster-corrected using 3dclustsim to an alpha level of p<.05. The cluster-level threshold was then multiple-comparisons corrected for the three seeds (i.e., p<.05/3 = 1.67*10−2). Anatomically defined delineations from the Ranta Frontal Lobe Atlas are shown for reference: orbitofrontal cortex is shown in the yellow, dorsolateral prefrontal cortex in red and blue, and medial prefrontal cortex in tan. Montreal Neurological Institute (MNI) coordinates are indicated above each slice.

We found significant age-diagnosis interactions on functional connectivity for two frontal-striatal pairs (executive striatum-OFC and limbic striatum-mPFC, p=0.005 and p=.003, respectively) (Figure 5). For the executive striatum-OFC, this interaction was driven by children in the TD group. A one-year increase in age was associated with a .07 increase in executive striatum-OFC functional connectivity (p=0.004) in TD children, but the relationship between executive striatum-OFC functional connectivity was not significant in the primary CMS group (beta = −0.04, p = 0.12). For the limbic striatum-mPFC, a one-year increase in age was associated with a .06 increase in limbic striatum-mPFC functional connectivity in the TD group (p=0.02) and a 0.05 decrease in functional connectivity (more negative functional connectivity) in the primary CMS group (p=0.01).

Figure 5: Age-diagnosis interactions for the prefrontal-striatal functional connectivity.

Figure 5:

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The relationship between prefrontal-striatal functional connectivity and age was observed in a sample of typically developing children (aqua dots) and children with motor stereotypies (red dots). Above each scatter plot, the regions of the frontal lobe from which connectivity scores were extracted are shown. In the CMS subjects, we observed statistically significant age-diagnosis interactions on functional connectivity for two pairs of ROIs (e.g., executive striatum-OFC and limbic striatum-OFC, p=0.005 and p=0.003 corrected, respectively). In contrast to the CMS subjects, TD group showed a positive correlation between age and functional connectivity pattern.

Lastly, no significant relationships were identified between frontal-striatal functional connectivity and SSS total score (p>.1 for all frontal-striatal pairs).

4. DISCUSSION:

This study, to our knowledge, represents the first comparison of functional connectivity in carefully diagnosed children with primary CMS and matched TD controls. Results showed reduced functional connectivity between prefrontal cortical and striatal regions (i.e., regions associated with goal-directed behaviors), but not frontal and striatal regions (i.e., regions implicated in habitual behaviors), in children with primary CMS as compared to TD peers. Specifically, children with primary CMS showed reduced connectivity between prefrontal regions (OFC and dlPFC) and the executive and limbic striatum and between the mPFC and the limbic striatum (see Figure 6). In addition, significant age-diagnosis interactions were identified in two of the prefrontal-striatal connections showing reduced connectivity. Having defined specific reductions in functional connectivity between prefrontal cortical regions and the striatum, one initial challenge is to define how these alterations could explain motor stereotypies through the alteration of established motor pathways.

Figure 6: Summary of observed functional connectivity differences.

Figure 6:

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Dashed lines indicate prefrontal-striatal pairs with weaker functional connectivity, on average, in the stereotypy group compared to the typically developing group. Solid lines indicate frontal-striatal pairs between which no group difference was observed.

Abbreviations: Dorsolateral prefrontal cortex (dlPFC), Medial prefrontal cortex (mPFC), Orbitofrontal cortex (OFC), Premotor cortex (PMC), Primary motor cortex (M1), Supplementary motor cortex (SMC).

4.1. Potential motor pathway disruptions:

One potential explanation is that the diminished prefrontal-striatal connectivity creates a functional imbalance between altered goal-directed (prefrontal-striatal) and an intact habitual (frontal-striatal) motor system. If present early in development, the delayed connectivity could contribute to the early appearance and persistence of complex motor stereotypies. Further, as development progresses, a greater reliance on the more posterior frontal to striatal circuitry (habitual pathway) could result in the persistence of motor stereotypies into adulthood. Recognizing, however, that five different prefrontal-striatal pairings showed reduced functional connectivity in children with CMS (i.e., two with executive striatum and three with limbic striatum), it remains to be determined which prefrontal changes are pathognomonic of primary CMS and which changes could possibly underlie comorbid or other conditions (e.g., ADHD, emotion, decision making, task-related actions (Gläscher et al., 2009; Moseley et al., 2015).

Although a simplistic functional imbalance hypothesis involving goal-directed and habitual pathways is certainly plausible, it is essential to recognize that increasing evidence supports the concept that these pathways are converging interactive systems. For example, consciously performed goal-directed behavior requires a prospective review of available options and judgment before action selection and execution. To achieve this outcome, regions of the cortex such as the vmPFC and OFC play a vital role in determining an action’s value, whereas the selection of an action from various contingencies requires input from the dorsolateral PFC, medial prefrontal cortex, sensory-motor cortex, and posterior-lateral parietal cortex (Funahashi, 2017; O’Doherty et al., 2017; Rudorf & Hare, 2014; Tanaka et al., 2008). This complexity of functional interactions is further emphasized in human studies investigating neural substrates in different situations, e.g., habit-like ‘slips of action’ (momentary loss of a goal when in a rush or distracted) (McNamee et al., 2015; Watson & de Wit, 2018). In this situation, the ‘slip’ recruits the lateral OFC, inferior frontal gyrus, anterior cingulate cortex, and paracingulate gyrus.

Studies in animal models further support interconnection and functional overlap between brain regions involved in goal-directed and habitual behaviors. Investigators examining mice’s motor cortex during a virtual maze navigating task showed that activity in the motor cortex was necessary for generating a response to unexpected events (i.e., loss of a goal/development of a new goal) (Heindorf et al., 2018). In addition, rodent models have shown that lesioning the dorsomedial striatum, a region facilitating goal-directed actions, resulted in increased habit-like responses (Corbit & Janak, 2010; Gremel & Costa, 2013). Whereas lesioning the dorsolateral striatum, a region associated with habitual behaviors changed habitual reward-seeking behaviors into goal-directed actions without altering goal-directed behaviors (Gremel & Costa, 2013; Yin et al., 2004). In contrast, other animal studies have shown that both the dorsal and ventral striatum modulate behavior in goal-directed tasks (Nishizawa et al., 2012) and can compensate for the loss of goal-directed and habitual functionality (Burton et al., 2014).

A second potential pathophysiological mechanism, based on the defined alteration in prefrontal functional connectivity, is a disruption of excitatory/ inhibitory systems (Aoki et al., 2019; Aron, 2011; Chabrol et al., 2019; Jahanshahi et al., 2015; Zandbelt & Vink, 2010). For example, with projections from the dorsolateral prefrontal cortex to caudate, the proactive inhibitory pathway cancels a prospective action (e.g., not doing an activity because one recognizes the consequences) (Aron, 2011; Jahanshahi et al., 2015; Zandbelt & Vink, 2010). In contrast, reactive inhibition, which involves projections from the inferior and dorsomedial frontal cortex to the subthalamic nucleus, is triggered by an external stimulus to block a motor response that is already in progress (e.g., the need to apply the brakes because the traffic light turns red) (Aron, 2011; Jahanshahi et al., 2015; Zandbelt & Vink, 2010). Further, in humans, a proposed, but as yet neuroanatomically undefined, voluntary inhibitory motor control system (Ganos et al., 2018) is also hypothesized to regulate complex interactions between sensory and motor cortices. Cortical-striatal alterations within the excitatory/inhibitory systems could explain a report showing that children with primary complex motor stereotypies have impaired reactive, but not proactive, inhibition (Mirabella et al., 2020).

Lastly, considering the potential effect of altered prefrontal cortical activity on motor stereotypies, one must also note the possibility of altered connectivity with as yet uninvestigated brain regions. Future studies are required to determine how aberrant specific frontal-striatal or frontal-’other brain’ regions contribute to the pathophysiology of motor stereotypies.

4.2. Delayed and/or aberrant development of prefrontal-striatal circuits?

Recognition of altered prefrontal-striatal development ((Figure 5A, C, E) raises the clinical question of whether this represents a delay in development or a fixed aberrant developmental anomaly. Although this study lacks a longitudinal analysis, functional imaging data did show significant age-diagnosis interactions in three of the altered prefrontal-striatal pairs (executive striatum-OFC, limbic striatum-OFC, and limbic striatum-mPFC). In the TD group, stronger positive functional connectivity, reflective of stronger functional integration, was observed in older compared to younger children. In contrast, in the motor stereotypy cohort, stronger negative connectivity, reflective of stronger functional segregation, was observed in older compared to younger children. This preliminary data raises the possibility of a continuing diminution in connectivity in the CMS cohort versus a strengthening in the TD group. Pathophysiologically, this data emphasizes the evolving nature of connectivity and raises the possibility of an aberrant, rather than merely delayed, development in primary CMS. Longitudinal studies would be of value in further addressing this issue.

4.3. Comparisons to results in secondary stereotypies:

As described, complex motor stereotypies can occur as both primary and secondary conditions. In studies evaluating fronto-stiatal circuitry in children with autistic spectrum disorder (ASD), results have been variable. For example, one study showed increased functional connectivity between frontal cortical-striatal regions (Di Martino et al., 2011), whereas another had decreased resting-state connectivity involving the prefrontal cortex (Padmanabhan et al., 2013). In a study assessing the association of repetitive behaviors in 50 ASD children (ages 8–17 years), increased repetitive behaviors were associated with reduced connectivity between frontoparietal cortical-limbic and motor cortex-limbic regions (Abbott et al., 2018). In summary, data from ASD studies are supportive of the hypothesis that repetitive behaviors likely represent an imbalance between corticostriatal circuits, rather than connectivity within individual circuits (Abbott et al., 2018).

4.4. Study limitations and future considerations:

Our findings, while robust and consistent, should be considered along with several study limitations: 1) This study focused solely on fronto-striatal connections and did not include M1-striatal, cortical-cortical interconnections or cortical-other (e.g., to thalamus, cerebellum) pairs. 2) although an ASD diagnosis was considered exclusionary a formal Autism Diagnostic Observation Schedule was not obtained. 3) Given the relatively small sample size, subgroup assessments based on precise age of onset, family history, and specific characteristics of the complex motor stereotypies was not feasible. It is certainly possible that inter-participant differences in movement sequences could affect functional connectivity findings involving motor regions. Heterogeneity and the relatively small sample size could also influence the ability to sufficiently examine the relationship between prefrontal/frontal-striatal functional connectivity and stereotypy severity. 4) The rsfMRI scan duration was relatively short (6.5 min). Several steps, however, were taken in line with recent recommendations to maximize data quality and minimize the influence of participant scan motion on functional connectivity estimates. 5) While all children in the study were medication-free (for at least three months) at the time of their resting-state scan, the impact of prior medication use was not assessed. 6) Future studies, including longitudinal protocols with younger populations (i.e., closer to the onset of symptoms), are critical to better understanding the unfolding of frontal lobe-striatal connectivity, the apparent imbalance between goal-directed and habit-related circuitry, developing inhibitory and other cortical connections, and ultimately their association with the persistence of complex motor stereotypies over time.

5. Conclusions:

Functional connectivity in children with primary complex motor stereotypies is altered in several prefrontal–striatal pathways, i.e., between OFC and dlPFC and the executive and limbic striatum and between mPFC and the limbic striatum. Since the aforementioned developmental delayed/or aberrant circuits are significant components of goal-directed movement pathways, persistent motor stereotypies could be the physiological result of an imbalance between goal-directed and habitual motor systems. However, recognizing that these developmentally altered systems are also active components of excitatory/inhibitory pathways and interact with other brain regions, additional pathophysiological hypotheses must also be considered. Future longitudinal studies are required to fully understand how alterations in prefrontal-striatal circuits contribute to primary complex motor stereotypies.

Acknowledgements:

This work was supported by the Nesbitt-McMaster Foundation, the Klump Family, the Graves Family and by grants from the National Institute of Mental Health (R01 MH085328, R01 MH078160, R01 MH106564), the Eunice Kennedy Shriver National Institutes of Child Health and Human Development (U54 HD079123), and the National Center for Research Resources Clinical and Translational Science Awards Program (UL RR025005).

Footnotes

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