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Resting-state brain networks in patients with Parkinson's disease and impulse control disorders
Accepted Manuscript Resting-state brain networks in patients with Parkinson’s disease and impulse control disorders Alessandro Tessitore, Gabriella Santangelo, Rosa De Micco, Alfonso Giordano, Simona Raimo, Marianna Amboni, Fabrizio Esposito, Paolo Barone, Gioacchino Tedeschi, Carmine Vitale PII:
S0010-9452(17)30197-1
DOI:
10.1016/j.cortex.2017.06.008
Reference:
CORTEX 2044
To appear in:
Cortex
Received Date: 1 February 2017 Revised Date:
5 May 2017
Accepted Date: 13 June 2017
Please cite this article as: Tessitore A, Santangelo G, De Micco R, Giordano A, Raimo S, Amboni M, Esposito F, Barone P, Tedeschi G, Vitale C, Resting-state brain networks in patients with Parkinson’s disease and impulse control disorders, CORTEX (2017), doi: 10.1016/j.cortex.2017.06.008. This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
ACCEPTED MANUSCRIPT Resting-state
brain
networks
in
patients
with
Parkinson’s
disease
and impulse control disorders
Alessandro Tessitorea,e*, Gabriella Santangelob,c, Rosa De Miccoa,e, Alfonso Giordanoa,e,
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Simona Raimob, Marianna Ambonic,d, Fabrizio Espositod, Paolo Baroned, Gioacchino Tedeschia,e, Carmine Vitalec,f
Department of Medical, Surgical, Neurological, Metabolic and Aging Sciences, University of
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a
Campania, Luigi Vanvitelli, Napoli, Italy
Neuropsychology Laboratory, Department of Psychology, University of Campania, Luigi
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b
Vanvitelli, Caserta, Italy c
Istituto di Diagnosi e Cura Hermitage-Capodimonte, Naples, Italy
d
Department of Medicine and Surgery, Neuroscience Section, University of Salerno, Baronissi
(SA), Italy e
MRI Research Center SUN-FISM, Second University of Naples, Naples, Italy
f
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*Corresponding author:
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Department of Motor Sciences and Health, University of Parthenope, Naples, Italy
Alessandro Tessitore MD, PhD
Department of Medical, Surgical, Neurological, Metabolic and Aging Sciences
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Second University of Naples
email: [email protected]
ACCEPTED MANUSCRIPT Abstract
Introduction: to investigate intrinsic neural networks connectivity changes in Parkinson's disease (PD) patients with and without Impulse Control Disorders (ICD).
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Methods: fifteen patients with PD with ICD (ICD+), 15 patients with PD without ICD (ICD-) and 24 age and sex-matched healthy controls (HC) were enrolled in the study. To identify patients with and without ICD and/or punding, we used the Minnesota Impulsive Disorders Interview (MIDI) and
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a clinical interview based on diagnostic criteria for each symptom. All patients underwent a detailed neuropsychological evaluation. Whole brain structural and functional imaging was performed on a
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3T GE MR scanner. Statistical analysis of functional data was completed using BrainVoyager QX software. Voxel-based morphometry (VBM) was used to test whether between-group differences in RS connectivity were related to structural abnormalities.
Results: The presence of ICD symptoms was associated with an increased connectivity within the
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salience and default-mode networks, as well as with a decreased connectivity within the central executive network (p<0.05 corrected). ICD severity was correlated with both salience and default mode networks connectivity changes only in the ICD+ group. VBM analysis did not reveal any
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statistically significant differences in local grey matter volume between ICD+ and ICD- patients and between all patients and HC (p<0.05. FWE).
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Conclusions: The presence of a disrupted connectivity within the three core neurocognitive networks may be considered as a potential neural correlate of ICD presence in patients with PD. Our findings provide additional insights into the mechanisms underlying ICD in PD, confirming the crucial role of an abnormal prefrontal-limbic-striatal homeostasis in their development.
Keywords: Parkinson’s disease, impulse control disorders, resting-state connectivity, reward system
ACCEPTED MANUSCRIPT 1. Introduction
Impulse control disorders (ICD) can be triggered by dopamine replacement therapies, especially dopamine-agonists, in patients with Parkinson’s disease (PD) (Weintraub et al., 2010). Converging
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evidence (Weintraub et al., 2013) suggest that PD itself does not confer an increased risk for development of ICD in the absence of treatment. On the other hand, only a specific subset of patients with PD will eventually develop ICD under dopaminergic treatment. When an ICD
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develops, therapeutic management can be very difficult. Therefore, a great deal of effort has gone in the recognition of clinical/pre-clinical, behavioral, neuropsychological and neural correlates of these
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behavioral symptoms. Several risk factors have been identified, such as younger age at PD onset, male sex, being unmarried, past or current depression, a positive family history of cigarette smoking or substance abuse (Weintraub et al., 2015). Moreover, PD patients with ICD seem to present a novelty seeking personality, an inclination for risk-taking behaviors with impaired decision-making
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and motor inhibition (see for a review Santangelo et al., 2017). Previous neuropsychological studies (Santangelo et al., 2009; Vitale et al., 2011) have also demonstrated that ICD in PD are associated with an altered cognitive profile, characterized by impaired cognitive flexibility and
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planning capability as well as by more inappropriate behavior and poor feedback processes. Evidence from previous brain metabolism (Cilia et al., 2011), functional and morphometric imaging
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studies (Frosini et al., 2010; Rao et al., 2010; Biundo et al., 2015; Carriere et al., 2015; Tessitore et al., 2016) have consistently demonstrated a dysfunction within the meso-cortico-limbic-striatal circuit in PD patients with ICD, involving both cortical and subcortical areas which are critical in the reward system, such as anterior cingulate cortex (ACC), orbitofrontal cortex (OFC), insula and ventral striatum (VS). Striatal (Steeves et al., 2009; O’Sullivan et al., 2011; Cilia et al., 2011; Politis et al., 2013) and extrastriatal (Ray et al., 2012) molecular imaging studies have also revealed the presence of an “hyperdopaminergic state” in the brain of PD patients with ICD, when exposed to reward stimuli. However, different task paradigms and controversial findings make the debate about
ACCEPTED MANUSCRIPT the role of different actors implicated in the pathogenesis of ICD in PD still open. Resting-state fMRI (rs-fMRI) allows for the exploration of brain connectivity between functionally linked cortical regions constituting resting-state networks (RSNs) (Barkhof et al., 2014). Therefore, by means of this fMRI approach it is possible to observe specific disease-related interferences on
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whole-brain functional connectivity without the potential bias from task-induced brain activations. To date, only one RS-fMRI study (Carriere et al., 2015) has investigated the neural correlates of ICD in PD, mainly focusing on the functional connectivity between striatum and cortical,
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subcortical regions. Therefore, in the present study we aimed to assess whether the presence of ICD
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in PD patients may determine abnormalities in the intrinsic neural networks connectivity.
2. Patients and Methods 2.1 Study population
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We screened consecutive outpatients attending the Movement Disorders Unit of the “Istituto di Diagnosi e Cura Hermitage –Capodimonte” in Naples, Italy. Imaging data were acquired at the MRI Research center of the University of Campania “Luigi Vanvitelli” in Naples, Italy. To be included
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in the study, the subjects had to meet the following criteria: 1) diagnosis of PD according to the
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United Kingdom Parkinson’s disease Society Brain Bank diagnostic criteria (Hughes et al., 1992) (2) lack of PD-related dementia according to published clinical criteria (Emre et al., 2007) (3) presence of ICD assessed by means of a modified version of the Minnesota Impulsive Disorders Interview (MIDI) (Weintraub et al., 2006, 2015) and a clinical interview. Exclusion criteria were: clinical signs satisfying criteria of possible atypical parkinsonisms (Wenning et al., 2011); secondary or iatrogenic parkinsonism; cerebral lesions on magnetic resonance imaging and/or computed tomography; severe concomitant diseases that might determine cognitive disturbances or brain metabolic alterations; history of or current psychiatric illness (i.e. hypomanic or manic episodes, psychosis, substance abuse, and attention deficit hyperactivity disorder [ADHD]); use of
ACCEPTED MANUSCRIPT typical and atypical antipsychotics in the last 2 month prior to enrolment. PD patients without dementia and not affected by ICD (as defined above), and matched to the study group for age, sex and educational level were enrolled as the control group. Moreover, we also recruited a group of healthy age and sex-matched controls (HC). Written informed consent was obtained from all
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subjects. The study was approved by the ethics committee of the University of Campania “Luigi Vanvitelli”, Naples, Italy.
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2.2 Procedures
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Patients with PD and controls underwent a comprehensive assessment of clinical, neuropsychiatric, neuropsychological functioning, and imaging sessions as described below. Clinical, neuropsychological and imaging assessments were performed in the morning, in the same day, in distinct sessions, when PD patients were in the ‘‘ON’’ state. Short breaks were introduced to avoid
2.3 Neurological evaluation
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fatigue.
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Patients underwent a neurological examination consisting of the motor section of the Unified Parkinsons’ Disease Rating Scale (UPDRS, section III) and the Hoehn and Yahr scale to measure
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the severity of motor symptoms in the “ON” state (Hoehn and Yahr, 1967). The levodopa equivalent daily dose (LEDD) was calculated both for dopamine agonists (LEDD-DA) and for dopamine agonists + L-dopa (total LEDD) (Tomlinson et al., 2010).
2.4 Neuropsychological and neuropsychiatric assessments A global cognitive evaluation was performed by means of the Mini Mental Status Examination (MMSE) in both patients and HC. All patients underwent a previously reported (Santangelo et al., 2009; Vitale et al., 2011) standardized neuropsychological evaluation to assess frontal
ACCEPTED MANUSCRIPT lobe/executive functions and memory. Patients were screened for ICD by the modified version of MIDI (Weintraub et al., 2006, 2015). It comprises five modules: compulsive buying (CB), pathological gambling (PG), hypersexuality (HS), compulsive eating (CE) and punding. Each module is composed by a screening general question to which the subject has to answer
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affirmatively (1 point) or negatively (0 points). If the screening question is answered in the negative, the interviewer moves to the next module. If it is answered in the affirmative, the
interviewer asks a series of additional questions reflecting DSM-IV criteria and pertaining to
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commonly reported thoughts, urges/desires, and behaviors associated with ICD. Each additional question is rated on a 4-point Likert scale (score 0–3 for each question) to measure the frequency of
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behaviors, thoughts, urges/desires associated with each ICD. In the present study, the total MIDI score was calculated and ranged from 0 to 56 (0-7 for CB; 0-13 for PG; 0-10 for HS; 0-10 for CE; 0-16 for punding), with a higher score indicating greater severity (i.e. frequency) of symptoms. The presence of ICD was defined as answering in the affirmative to 1 (compulsive sexual behavior and
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compulsive shopping) or 2 (compulsive gambling) gateway questions plus an affirmative answer to ≥1 of the remaining questions of the relevant ICD module of the MIDI. Screening procedures were administered to patients and, separately, to their caregivers. Patients with a direct and/or indirect
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report of any ICD during the previous 2 months were considered positive at screening and underwent a specific interview based on the clinical diagnostic criteria for PG and HS. Since
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diagnostic criteria for CE are absent, we followed the definition of this condition reported in Nirenberg and Waters (2006) in which the CE was defined as an uncontrollable consumption of a larger amount of food than normal, in excess of that necessary to alleviate hunger. Moreover, to detect the occurrence of punding, we followed the definition provided by Fernandez and Friedman (1999). The punding is defined as an intense fascination with complex, excessive, repetitive, nongoal oriented behaviors including less complex acts such as shuffling papers, reordering bricks or sorting handbags or more complex acts such as hobbysm, writing or excessive computer use.
ACCEPTED MANUSCRIPT Patients and HC underwent a supplementary neuropsychiatric examination consisting of: (1) the Hamilton Depression Rating Scale (HAM-D) to measure severity of depressive symptoms and to identify clinically significant depression (Hamilton, 1960); (2) the Hospital Anxiety and Depression Scale (HADS), to measure severity of anxiety and depression (Leentjens et al., 2011) and (3) an
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interview based on DSM-IV criteria for hypomanic or manic episodes, psychosis, substance abuse, and ADHD. HC were also screened for the presence of ICD by means of a clinical interview based
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on DSM-IV criteria.
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2.5 Imaging Parameters
All subjects were scanned in the morning and patients were all in the “on-medication” state. Magnetic resonance images were acquired on a 3 T GE scanner equipped with an 8-channel parallel head coil. fMRI data consisted of 240 volumes of a repeated gradient-echo echo planar imaging
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T2*-weighted sequence (TR = 1508 ms, axial slices = 29, matrix = 64 x 64, field of view = 256 mm, thickness = 4 mm, interslice gap = 0mm). During the functional scan, subjects were asked to simply stay motionless, awake, with their eyes closed. Three-dimensional high-resolution T1-
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weighted sagittal images (GE sequence IR-FSPGR, TR = 6988 ms, TI = 1100 ms, TE = 3.9 ms, flip angle = 10, voxel size = 1 x 1 x 1.2 mm3) were acquired for registration and normalization of
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the functional images.
2.6 Statistical analysis of clinical, motor and neuropsychological data Differences in the distribution of categorical variables among groups were assessed by means of chi-square. For the analysis of differences among groups on demographic, clinical, behavioral (HADS and HAM-D) and neuropsychological variables we used non-parametric tests (Kruskal– Wallis H test to compare three samples, and the Mann–Whitney U test to compare two samples) to
ACCEPTED MANUSCRIPT avoid biases due to the small sample size. A p value less than 0.05 was considered statistically significant. Analyses were performed with SPSS version 13 (SPSS Inc. Chicago, IL).
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2.7 Resting-state fMRI pre-processing and statistical analysis Image data pre-processing and statistical analysis were performed with BrainVoyager QX (Brain Innovation BV, The Netherlands). Before statistical analyses, individual functional data were coregistered to their own anatomical data and spatially normalized to the standard Talairach space.
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Single-subject and group-level ICA was carried out respectively with the fastICA (Hyvarinen et al.,
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2001) and the self-organizing group ICA (sogICA) algorithms (Esposito et al., 2005). For each subject, 40 independent components were extracted. All single-subject component maps from all subjects were then “clustered” at the group level, resulting in 40 single-group average maps that were visually inspected to recognize the major physiological resting-state networks. The selection of the clusters of interest implied identifying in each group component map the presence of
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anatomically relevant areas that, jointly and consistently across subjects, reproduced the layouts of the major physiological RSNs.
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The most reported and investigated RSNs are the default mode network (DMN), the left and right central executive networks (CEN), the salience network (SN), the sensorimotor network (SMN) and
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the visual and auditory networks (Damoiseaux et al., 2006). For homologue components corresponding to a given RSN, a multi-subject random effects (RFX) analysis was carried out that treated the individual subject map values as random observations at each voxel. Single-group onesample t-tests were used to map the whole-brain distribution of the components of interest (p=0.05, cluster-level corrected) in each patient group and in the control group. An inclusive mask was created from the single-group component maps to define the search volume for within-network group comparisons (i.e. ICD+ vs ICD-, ICD+ vs HC and ICD- vs HC). Cognitive outcomes, LEDD and age were used as additional covariates in the comparison between ICD+ and ICD- group. Then, a two-sample t-test was computed at each voxel of the mask to produce a t-map of the differences
ACCEPTED MANUSCRIPT that was applied a minimum threshold of p=0.05 (corrected for multiple comparisons). To correct for multiple comparisons, regional effects were only accepted for clusters exceeding a minimum size determined with a non-parametric randomization approach. Namely, an initial voxel-level threshold was set to p=0.01 (uncorrected) and a minimum cluster size was estimated after 1000
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Montecarlo simulations that protected against false positive clusters up to 5%.
Individual ICA z-scores for both the patient groups and the control group were extracted from regions identified in the above analyses and used for all between-group comparisons and for linear
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correlation analyses with MIDI scores and other clinical and neuropsychological variables.
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2.8 VBM analysis
Data were processed and examined using SPM8 software (Wellcome Trust Centre for Neuroimaging, London, UK; http://www.fil.ion.ucl.ac.uk/spm), where we applied VBM implemented in the VBM8 toolbox (http://dbm.neuro.uni-jena.de/vbm.html) with default
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parameters incorporating the DARTEL toolbox, which was used to obtain a high-dimensional normalization protocol (Ashburner, 2007). Images were bias-corrected, tissue-classified, and registered using linear (12-parameter affine) and non-linear transformations (warping) within a
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unified model (Ashburner, 2007). Subsequently, the warped grey matter (GM) segments were affine-transformed into Montreal Neurological Institute (MNI) space and were scaled by the
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Jacobian determinants of the deformations to account for the local compression and stretching that occurs as a consequence of the warping and affine transformation (modulated GM volumes. Finally, the modulated volumes were smoothed with a Gaussian kernel of 8-mm full-width at half maximum (FWHM). The GM volume maps were statistically analyzed using the general linear model based on Gaussian random field theory. Statistical analysis consisted of an ANCOVA with total intracranial volume (TIV), gender, age, neuropsychological data differing between patients with and without ICD and LEDD as covariates of no interest. Statistical inference was performed at the voxel level, with a family-wise error (FWE) correction for multiple comparisons (p<0.05).
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3. Results 3.1 Clinical, behavioral and cognitive findings
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Fifteen patients with PD with ICD (ICD+), 15 patients with PD without ICD (ICD-) and 24 age and sex-matched HC were enrolled in the study.
The clinical and demographic characteristics of patients are shown in Table 1. No significant differences were detected between ICD+ and ICD- patients on the demographic, clinical and LEDD
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variables. As expected, ICD+ patients differed significantly (p<0.05) from ICD- patients on MIDI
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scores, with HS (87%) and CE (54%), alone or combined, most commonly recorded; PG was found as less frequent behavior disorder (7%) in our patients. Moreover, ICD+ compared to ICD- patients performed significantly worse (p<0.05) on neuropsychological tests exploring both executive and memory functions (see Table 2). The severity of affective disorders (HAM-D and HADS) did not differ between PD patient subgroups (see Tables 1-2 for cognitive and behavioral data). No PD
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patient or HC was affected by anxiety disorder, mania, hypomania, or bipolar disorder.
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3.2 Imaging data and correlation analysis
By visually inspecting ICA-derived components of RS-fMRI data for each group (ICD+ and ICD-,
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HC), we identified RSNs components using similar methodology as that of previous studies (Damoiseaux et al., 2006). Significant functional connectivity changes between ICD+ and ICDpatients were identified within the following RSNs (see Fig. 1): 1. SN: ICD+ patients showed increased component time course-related activity compared to ICDpatients in the bilateral insula (right: x=40; y=-6; z=2; left: x=-39; y=-9; z=-1) and right VS (x=9; y=2; z=8); 2. CEN: at the level of the right CEN, ICD+ patients showed decreased component time courserelated activity compared to ICD- patients in the dorsolateral prefrontal cortex (DLPFC, BA 9; x=32; y=-74; z=36) and the inferior parietal cortex (IPL, BA 40; x=44; y=1; z=22).
ACCEPTED MANUSCRIPT 3. DMN: ICD+ patients showed increased component time course-related activity compared to ICD- patients in the left middle temporal gyrus (BA19; x=-51; y=-73; z=25). The comparison between ICD+ and HC (data not shown) revealed the following statistically significant changes: ICD+ patients showed decreased component time course-related activity in the
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anterior cingulate cortex (BA24, x=-3; y=29; z=19) and in the precuneus (x=-15, y=-55, z=25) within the SN and DMN, respectively. Moreover, ICD+ patients showed increased component time course-related activity within the right CEN in the right angular gyrus (x=42, y=-73, z=31).
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The comparison between ICD- and HC (data not shown) revealed the following statistically significant changes: ICD- patients showed decreased component time course-related activity in the
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anterior cingulate cortex (BA24, x=-3; y=29; z=19) within the SN. Within the DMN, ICD- showed decreased component time course-related activity in the left middle temporal gyrus (x=-48, y=-64, z=25) and in the precuneus (x=-15, y=-55, z=25). Moreover, ICD- patients showed increased component time course-related activity within the right CEN in the right angular gyrus (x=42, y=-
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73, z=31).
Correlation analyses revealed that MIDI scores were positively correlated with DMN (R=0.8; p = 0.003) and SN (R=0.6 ; p=0.02) connectivity changes only in the ICD+ group (see Fig. 2). No
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correlation was found between any other clinical, demographic or cognitive measures and imaging data.
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VBM data did not reveal any statistically significant differences in local GM between ICD+ and ICD- patients and between all patients and HC (p<0.05. FWE).
4. Discussion Our imaging data demonstrated that ICD+ patients exhibit a reduced functional brain connectivity within the CEN and an increased connectivity within both SN and DMN compared to ICD- patients. Notably, these functional abnormalities were correlated with ICD severity and were independent
ACCEPTED MANUSCRIPT from motor, cognitive features and dopaminergic load. This divergent pattern may represent a neural correlate of the presence of ICD symptoms in PD patients. SN, CEN and DMN are three large-scale networks that play a pivotal role in cognitive and affective information processing (Sridharan et al., 2008; Bressler and Menon, 2010). The SN is a limbic-
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paralimbic network encompassing cortical and subcortical hubs involved in affective, reward processing and interoceptive awareness, such as the anterior insula, the ACC and the VS (Seeley et al., 2007; Menon and Uddin, 2010). The SN is thought to exert an important function in orienting
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attention to behaviorally salient and rewarding stimuli and facilitating goal-directed behavior (Cai et al., 2016; Menon et al., 2015). The CEN encompasses mainly the PFC and IPL and is critical to
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process external, attentionally driven executive functions as well as for judgment and decisionmaking (Miller and Cohen, 2001; Petrides, 2005; Koechlin and Summerfield, 2007). Finally, the DMN includes the posterior cingulate, medial prefrontal and medial temporal cortices, and is involved in ruminations, mind-wandering, emotional processing and cognitive social functions
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(Buckner et al., 2008; Spreng et al., 2009). Previous functional connectivity studies have demonstrated that CEN and DMN time-courses are usually anti-correlated (Fox al., 2005; Menon, 2011). In response to external stimuli the CEN is activated whereas the DMN is deactivated,
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following an antagonism which seems to be critical to generate and maintain an efficient behavioral and cognitive performance (Menon et al., 2011). The SN has shown to orchestrate a dynamic
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switching between the DMN and CEN, engaging the CEN while disengaging the DMN when an accurate control is needed to effort a cognitive demanding task (Menon and Uddin, 2010; Menon, 2011). During rest, the SN might contribute to maintain the DMN hypoconnected while the CEN is hyperconnected, allowing an individual to remain prepared to unexpected environmental events (Fox et al., 2005). In the present study, the SN showed an increased connectivity in the ICD+ group which was positively correlated with symptoms severity (as assessed by MIDI scores). Both these findings are not surprising, considering the abovementioned role of crucial SN hubs in the reward system. Particularly, the VS may be considered as an interface of cortical and limbic inputs and
ACCEPTED MANUSCRIPT relies on downstream structures to drive reward-related actions (Haber and Knutson, 2010). Indeed, an abnormal activity within this structure has been also found in several task-related imaging studies in PD patients with impulse controls behaviors (Rao et al., 2010). Similarly, the insula has been proposed as the core node acting in detection and selection of salient events, and starting
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additional processing to generate an efficient behavioral response (Singer et al., 2009; Menon and Uddin, 2010). Remarkably, an abnormal connectivity within the SN has been revealed also in many addiction disorders, like substance-related disorders (Qiu et al., 2017), nicotine addiction (Fedota
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and Stein, 2015; Li et al., 2016) and online gaming disorder (Zhang et al., 2016), revealing an impaired connectivity within cortical areas linked to impulsivity and behaviors control. Therefore,
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our finding of an increased connectivity within the SN may itself rely on reward system and decision-making aberration in PD patients with ICD. Furthermore, the presence of a disrupted connectivity within the SN may also lead to an aberrant regulatory role and functional coupling with the DMN and CEN. However, since we did not directly investigate the between-network
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connectivity, our findings cannot support this hypothesis. It is noteworthy that a similar pattern of functional reorganization has been consistently observed in affective disorders (see for a review Mulders et al., 2015) and has been correlated to the presence of an increased ruminative state
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(Greicius et al., 2007). Similarly, we could speculate that the hyperconnected DMN and a reduced CEN connectivity may drive the ICD+ patient to focus on endogenous stimuli. Conversely, external
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stimuli, such as environment and social context, are not considered as prominent anymore whereas the patient is internally-oriented ruminating about his compulsion. Consequently, processing and evaluation of possible outcomes and judging about consequences, which are critical to efficiently drive human behaviors, might be profoundly impaired by the interference of the DMN. Our hypothesis is corroborated by the positive correlations between DMN functional connectivity and ICD symptoms severity (i.e. the more the patient is wandering through self-referential thoughts the more severe are the ICD). On the other hand, decreased connectivity within the CEN may rely on altered risk/benefit evaluation and decision-making. Interestingly, CEN functional connectivity
ACCEPTED MANUSCRIPT and CEN/SN connectivity strength have been shown to protect against relapse to cocaine use following treatment, likely reflecting a better capacity to perform an executive control process when facing reward stimuli induced by cocaine (McHugh et al., 2016). Accordingly, PD patients with ICD are characterized by impaired cognitive flexibility and behavioural planning as evidenced by
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poor performance on Trial making test and other frontal tasks (Santangelo et al., 2009; Vitale et al., 2011, Santangelo et al., 2013). Considering as a whole both neuropsychological and imaging findings, ICD patients would have an impaired ability in selecting, monitoring and shifting from
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disadvantageous alternatives to more advantageous ones, thus perseverating socially and personally non-adaptive behaviors.
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It is noteworthy that a disrupted functional connectivity within SN, DMN, and CEN have been also implicated in other addiction disorders and in abstinence pathophysiology (Pariyadath et al., 2016; Westlye et al., 2016; Qiu et al., 2017). In keeping with these findings the presence of a disrupted connectivity within these three core neurocognitive networks may be considered as a potential
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neural correlate of ICD development in PD patients. Which factor could primarily trigger this functional networks reorganization in ICD patients is still a matter of discussion. A possible explanation could come from several neurobehavioral studies on reward system regulation. Indeed,
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it has been observed that prefrontal inputs on the VS enable the ability to shift behavioral focus as cues salience change (Goto and Grace, 2005; Sesack and Grace, 2010) whereas hippocampal
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connections contribute to keep the individual focused on the task (Maren, 1999; Fanselow, 2000). These functions are regulated by dopamine firing: prefrontal connections are inhibited by dopamine via D2-receptors, whereas hippocampal connections are activated by dopamine via D1-receptors. Therefore, it has been proposed that when dopaminergic system is overload, as it might occur in ICD+ patients under dopaminergic treatment, prefrontal influence on VS may be disabled whereas the hippocampal drive is reinforced (Napier et al., 2015). This condition might make the patients consistently focused on the reward stimuli which have primarily induced the dopamine release, looping the reward-system (Napier et al., 2015). Thus, we hypothesize that this dopaminergic
ACCEPTED MANUSCRIPT overstimulation might underlie the observed increased connectivity within the SN and DMN as well as the decreased connectivity within the CEN. This functional phenomenon is not driven by the dopaminergic treatment load, as no difference has been found in both LEDD and LEDD-DA between the two study groups. In previous PET 11C-raclopride studies (Steeves et al., 2009;
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O’Sullivan et al., 2011), ICD+ patients have been shown to present a greater decrease in binding potential in the VS during reward cues and tasks compared to ICD- patients and controls, likely reflecting a pre-existing “hyperdopaminergic state”. This “hyperdopaminergic state” may affect the
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dopaminergic drive within the reward system, as discussed above. Thus, further investigations are needed to clarify whether predisposing genetic (Lee et al., 2012; Vallelunga et al., 2012),
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enviromental (Voon et al., 2007) or neuropsychological (Antonini et al., 2011) factors may induce this high dopaminergic response to reward stimuli in the cortico-striato-cortical pathway, which may represent a pre-existing vulnerability to eventually develop ICD after dopaminergic replacement therapy.
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The observed connectivity abnormalities were detected in the absence of statistically significant GM changes in the same regions. Moreover, in a previous morphometric study (Tessitore et al., 2016) on the same PD population, we did not find any corticometric change in the same
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functionally disrupted regions. Future studies are needed to better clarify the relationship between functional and structural abnormalities underlying ICD in PD, which can be both influenced by the
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chronic dopaminergic treatment. The present study has some limitations worth noting. First, although our patients subgroups were clinically well matched, our sample size was relatively small. Second, we have not used the questionnaire for impulsive-compulsive disorders in Parkinson's disease rating scale (QUIP-RS) (Weintraub et al., 2012). However, to our knowledge this scale has not been yet validated in Italian and has been developed after our study recruitment initiation. These limitations notwithstanding, our findings provide additional insights into the mechanisms underlying ICD development in PD.
ACCEPTED MANUSCRIPT Future longitudinal studies including larger PD populations with ICD in an early phase of the
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disease are needed to verify these observations.
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Figure 1 Resting-state network connectivity changes.
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Figure captions
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Whole-brain significant connectivity differences between ICD+ and ICD- patients (p<0.05
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corrected) within central executive, default-mode and salience networks and bar graphs of the average ICA z-scores (IPL, ICD+: 1.8±1.3; ICD-: 3.1±1.5; p=0.01; DLPFC, ICD+: 0.5±0.5; ICD-: 1.5±1.3; p=0.007; MTG, ICD+: 0.3±1.1; ICD-: -0.4±0.5; p=0.04; VS, ICD+: 0.8±1.5; ICD-: 0.2±0.9; p=0.03; l-INS, ICD+: 1.3±1.3; ICD-: 0.3±0.8; p=0.03; r-INS, ICD+: 0.9±1; ICD-: 0.2±1.1;
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p=0.04).
IPL: inferior parietal; DLPFC: dorsolateral prefrontal cortex; MTG: middle temporal gyrus; VS:
Figure 2
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ventral striatum; l-INS: left insula; r-INS: right insula.
patients.
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Correlation analyses between MIDI scores and SN and DMN average ICA z-scores in ICD+
ACCEPTED MANUSCRIPT Disclosure Statement Authors state that there are no actual or potential conflicts of interest and no relevant financial
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support. All authors have approved the final article.
ACCEPTED MANUSCRIPT Table 1: Demographic, clinical and behavioural features of ICD+ and ICD- patients and HC HC (n=24) mean±SD (mean rank)
ICD+ (n=15) mean±SD (mean rank)
ICD(n=15) mean±SD (mean rank)
Age
63.54±6.7 (28.4)
62.8±8.6 (27.1)
63.1±8 (26.4)
Education
10.3±3.7 (24.6)
9.8±5 (22.1)
12.9±8 (31.9)
17/7
13/2
12/3
HAM-D
7.2±2.7 (25.6)
8.6±4.7 (27.7)
HADS
9.6±3.7 (27.8)
11.3±8.2 (27.5)
n.a
H&Y stage
n.a
UPDRS III
n.a
Total LEDD (mg daily)
MIDI score
- HS - CE - PG
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Frequency of ICD
n.a.
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DA LEDD (mg daily)
n.a
- Co-occurrence of HS and CE
n.a.
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0.1
1.3
0.2
7.4 ±3.9 (24.7)
0.2
0.8
8.1±7.5 (19.7)
3.0
0.2
Mann-Whitney U test
5.3±2.9 (11.9)
6.6±3.9 (15.8)
64.5
0.2
1.3±0.5 (13.5)
1.4±0.6 (14.4)
84.5
0.7
10.9±4.5 (13.3)
12.1±4.4 (14.6)
82.5
0.6
70.5
0.3
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Disease Duration
0.9
3.4
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Gender (M/F)
0.1
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Kruskal-Wallis p -value test/χ2
477.3 ±222.9 532.1±207.2 (12.4) (15.4) 243.3±82.1 (12.5)
243.3±90.2 (15.3)
73.0
0.3
6.8±3.2 (21.5)
0.4±0.5 (7.5)
0.0
<0.001
n 6 4 1 4
H&Y stage: Hoehn & Yahr stage; UPDRS: Unified Parkinson’s Disease Rating Scale; HAM-D: Hamilton Depression Rating Scale; HADS: Hospital Anxiety and Depression Scale; MIDI: Minnesota Impulsive Disorders Interview; LEDD: Levodopa Equivalent Daily Dose; DA: dopamine agonist; HS: Hypersexuality; CE: Compulsive Eating; PG: Pathological Gambling; n.a. = not available; χ2=chi-squared. The p values of post-hoc analysis (by Mann-Whitney U test) for comparison between HC and ICD+ are the following: 0.743 for age; 0.582 for education; 0.254 for gender; 0.718 for HAM-D; 0.960 for HADS. The p values of post-hoc analysis (by Mann-Whitney U test) for comparison between HC and ICD- are the following: 0.731 for age; 0.135 for education; 0.524 for gender; 0.846 for HAM-D; 0.082 for HADS. The p values of post-hoc analysis (by Mann-Whitney U test) for comparison between ICD+ and ICD- are the following: 0.914 for age; 0.116 for education; 0.624 for gender; 0.583 for HAM-D; 0.231 for HADS.
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Significant differences were reported in bold.
ACCEPTED MANUSCRIPT Table 2: Cognitive evaluation of PD patients and HC
28.2±1.8 n.a. n.a.
Corsi’s test
n.a.
WCST-global score
n.a.
ROCF-copy task
n.a.
RCPM
n.a.
VF
n.a.
SF
n.a.
TMT:B
n.a.
Interference task of Stroop Test Attentional matrices
n.a.
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Verbal delayed recall
n.a.
ICD(n=15) mean±SD (mean rank) 27.1±1.8 43.8±11.4 (17.25) 9±3 (16.65) 5.1±0.9 (15.86) 82.1±30.3 (11.07) 29.2±4.8 (15.71) 28.7±5.5 (17.39) 37.5 ± 10.4 (17.75) 20 ± 4.3 (17.93) 119.2 ± 68.5 (10.86) 13.9 ± 7.5 (15.89) 51.5 ± 6.4 (16.11)
KruskalWallis/MannWhitney test
p-value
75.5 45.5
0.452 0.027
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MMSE Verbal immediate recall
ICD+ (n=15) mean±SD (mean rank) 26.5±2.2 33.6±7.2 (10.50) 6.3±2.6 (10.35) 4.5±0.9 (12.0) 101.4±34.3 (17.15) 24.6±8.9 (12.15) 22.2±7.9 (10.35) 24.5 ± 13.4 (9.96) 15.1 ± 5.7 (9.77) 181.1 ± 98.6 (17.38) 10.5 ± 7.8 (11.96) 45.8 ± 11.3 (11.73)
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HC (n=24) mean±SD
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Cognitive Tasks
43.5
0.034
65.0
0.220
50.0
0.045
62.5
0.165
43.5
0.021
38.5
0.009
36.0
0.007
47.0
0.033
64.5
0.197
61.5
0.151
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MMSE: Mini Mental State Examination; WCST: Wisconsin Card Sorting Test; ROCF: Rey-Osterrieth Complex Figure Test; RCPM: Raven’s 47 Coloured Progressive Matrices; VF: Phonological verbal fluency; SF: Semantic Fluency; TMT-B: part B of the Trail Making Test. The p values of post-hoc analysis (by Mann-Whitney U test) on MMSE were the following: 0.005 for comparison between HC and ICD+; 0.067 for comparison between HC and ICD-; 0.259 for comparison between ICD+ and ICD-. Significant differences were reported in bold.
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ACCEPTED MANUSCRIPT Highlights We evaluated resting-state functional connectivity changes in PD patients with ICD.
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ICD+ patients exhibit a disrupted connectivity within DMN, SN and CEN networks.
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ICD severity was associated with functional connectivity changes within DMN and SN.
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We confirm the role of the prefrontal-limbic-striatal pathway in ICD development.
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S0010-9452(17)30197-1
DOI:
10.1016/j.cortex.2017.06.008
Reference:
CORTEX 2044
To appear in:
Cortex
Received Date: 1 February 2017 Revised Date:
5 May 2017
Accepted Date: 13 June 2017
Please cite this article as: Tessitore A, Santangelo G, De Micco R, Giordano A, Raimo S, Amboni M, Esposito F, Barone P, Tedeschi G, Vitale C, Resting-state brain networks in patients with Parkinson’s disease and impulse control disorders, CORTEX (2017), doi: 10.1016/j.cortex.2017.06.008. This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
ACCEPTED MANUSCRIPT Resting-state
brain
networks
in
patients
with
Parkinson’s
disease
and impulse control disorders
Alessandro Tessitorea,e*, Gabriella Santangelob,c, Rosa De Miccoa,e, Alfonso Giordanoa,e,
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Simona Raimob, Marianna Ambonic,d, Fabrizio Espositod, Paolo Baroned, Gioacchino Tedeschia,e, Carmine Vitalec,f
Department of Medical, Surgical, Neurological, Metabolic and Aging Sciences, University of
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a
Campania, Luigi Vanvitelli, Napoli, Italy
Neuropsychology Laboratory, Department of Psychology, University of Campania, Luigi
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b
Vanvitelli, Caserta, Italy c
Istituto di Diagnosi e Cura Hermitage-Capodimonte, Naples, Italy
d
Department of Medicine and Surgery, Neuroscience Section, University of Salerno, Baronissi
(SA), Italy e
MRI Research Center SUN-FISM, Second University of Naples, Naples, Italy
f
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*Corresponding author:
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Department of Motor Sciences and Health, University of Parthenope, Naples, Italy
Alessandro Tessitore MD, PhD
Department of Medical, Surgical, Neurological, Metabolic and Aging Sciences
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Second University of Naples
email: [email protected]
ACCEPTED MANUSCRIPT Abstract
Introduction: to investigate intrinsic neural networks connectivity changes in Parkinson's disease (PD) patients with and without Impulse Control Disorders (ICD).
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Methods: fifteen patients with PD with ICD (ICD+), 15 patients with PD without ICD (ICD-) and 24 age and sex-matched healthy controls (HC) were enrolled in the study. To identify patients with and without ICD and/or punding, we used the Minnesota Impulsive Disorders Interview (MIDI) and
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a clinical interview based on diagnostic criteria for each symptom. All patients underwent a detailed neuropsychological evaluation. Whole brain structural and functional imaging was performed on a
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3T GE MR scanner. Statistical analysis of functional data was completed using BrainVoyager QX software. Voxel-based morphometry (VBM) was used to test whether between-group differences in RS connectivity were related to structural abnormalities.
Results: The presence of ICD symptoms was associated with an increased connectivity within the
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salience and default-mode networks, as well as with a decreased connectivity within the central executive network (p<0.05 corrected). ICD severity was correlated with both salience and default mode networks connectivity changes only in the ICD+ group. VBM analysis did not reveal any
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statistically significant differences in local grey matter volume between ICD+ and ICD- patients and between all patients and HC (p<0.05. FWE).
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Conclusions: The presence of a disrupted connectivity within the three core neurocognitive networks may be considered as a potential neural correlate of ICD presence in patients with PD. Our findings provide additional insights into the mechanisms underlying ICD in PD, confirming the crucial role of an abnormal prefrontal-limbic-striatal homeostasis in their development.
Keywords: Parkinson’s disease, impulse control disorders, resting-state connectivity, reward system
ACCEPTED MANUSCRIPT 1. Introduction
Impulse control disorders (ICD) can be triggered by dopamine replacement therapies, especially dopamine-agonists, in patients with Parkinson’s disease (PD) (Weintraub et al., 2010). Converging
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evidence (Weintraub et al., 2013) suggest that PD itself does not confer an increased risk for development of ICD in the absence of treatment. On the other hand, only a specific subset of patients with PD will eventually develop ICD under dopaminergic treatment. When an ICD
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develops, therapeutic management can be very difficult. Therefore, a great deal of effort has gone in the recognition of clinical/pre-clinical, behavioral, neuropsychological and neural correlates of these
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behavioral symptoms. Several risk factors have been identified, such as younger age at PD onset, male sex, being unmarried, past or current depression, a positive family history of cigarette smoking or substance abuse (Weintraub et al., 2015). Moreover, PD patients with ICD seem to present a novelty seeking personality, an inclination for risk-taking behaviors with impaired decision-making
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and motor inhibition (see for a review Santangelo et al., 2017). Previous neuropsychological studies (Santangelo et al., 2009; Vitale et al., 2011) have also demonstrated that ICD in PD are associated with an altered cognitive profile, characterized by impaired cognitive flexibility and
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planning capability as well as by more inappropriate behavior and poor feedback processes. Evidence from previous brain metabolism (Cilia et al., 2011), functional and morphometric imaging
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studies (Frosini et al., 2010; Rao et al., 2010; Biundo et al., 2015; Carriere et al., 2015; Tessitore et al., 2016) have consistently demonstrated a dysfunction within the meso-cortico-limbic-striatal circuit in PD patients with ICD, involving both cortical and subcortical areas which are critical in the reward system, such as anterior cingulate cortex (ACC), orbitofrontal cortex (OFC), insula and ventral striatum (VS). Striatal (Steeves et al., 2009; O’Sullivan et al., 2011; Cilia et al., 2011; Politis et al., 2013) and extrastriatal (Ray et al., 2012) molecular imaging studies have also revealed the presence of an “hyperdopaminergic state” in the brain of PD patients with ICD, when exposed to reward stimuli. However, different task paradigms and controversial findings make the debate about
ACCEPTED MANUSCRIPT the role of different actors implicated in the pathogenesis of ICD in PD still open. Resting-state fMRI (rs-fMRI) allows for the exploration of brain connectivity between functionally linked cortical regions constituting resting-state networks (RSNs) (Barkhof et al., 2014). Therefore, by means of this fMRI approach it is possible to observe specific disease-related interferences on
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whole-brain functional connectivity without the potential bias from task-induced brain activations. To date, only one RS-fMRI study (Carriere et al., 2015) has investigated the neural correlates of ICD in PD, mainly focusing on the functional connectivity between striatum and cortical,
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subcortical regions. Therefore, in the present study we aimed to assess whether the presence of ICD
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in PD patients may determine abnormalities in the intrinsic neural networks connectivity.
2. Patients and Methods 2.1 Study population
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We screened consecutive outpatients attending the Movement Disorders Unit of the “Istituto di Diagnosi e Cura Hermitage –Capodimonte” in Naples, Italy. Imaging data were acquired at the MRI Research center of the University of Campania “Luigi Vanvitelli” in Naples, Italy. To be included
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in the study, the subjects had to meet the following criteria: 1) diagnosis of PD according to the
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United Kingdom Parkinson’s disease Society Brain Bank diagnostic criteria (Hughes et al., 1992) (2) lack of PD-related dementia according to published clinical criteria (Emre et al., 2007) (3) presence of ICD assessed by means of a modified version of the Minnesota Impulsive Disorders Interview (MIDI) (Weintraub et al., 2006, 2015) and a clinical interview. Exclusion criteria were: clinical signs satisfying criteria of possible atypical parkinsonisms (Wenning et al., 2011); secondary or iatrogenic parkinsonism; cerebral lesions on magnetic resonance imaging and/or computed tomography; severe concomitant diseases that might determine cognitive disturbances or brain metabolic alterations; history of or current psychiatric illness (i.e. hypomanic or manic episodes, psychosis, substance abuse, and attention deficit hyperactivity disorder [ADHD]); use of
ACCEPTED MANUSCRIPT typical and atypical antipsychotics in the last 2 month prior to enrolment. PD patients without dementia and not affected by ICD (as defined above), and matched to the study group for age, sex and educational level were enrolled as the control group. Moreover, we also recruited a group of healthy age and sex-matched controls (HC). Written informed consent was obtained from all
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subjects. The study was approved by the ethics committee of the University of Campania “Luigi Vanvitelli”, Naples, Italy.
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2.2 Procedures
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Patients with PD and controls underwent a comprehensive assessment of clinical, neuropsychiatric, neuropsychological functioning, and imaging sessions as described below. Clinical, neuropsychological and imaging assessments were performed in the morning, in the same day, in distinct sessions, when PD patients were in the ‘‘ON’’ state. Short breaks were introduced to avoid
2.3 Neurological evaluation
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fatigue.
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Patients underwent a neurological examination consisting of the motor section of the Unified Parkinsons’ Disease Rating Scale (UPDRS, section III) and the Hoehn and Yahr scale to measure
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the severity of motor symptoms in the “ON” state (Hoehn and Yahr, 1967). The levodopa equivalent daily dose (LEDD) was calculated both for dopamine agonists (LEDD-DA) and for dopamine agonists + L-dopa (total LEDD) (Tomlinson et al., 2010).
2.4 Neuropsychological and neuropsychiatric assessments A global cognitive evaluation was performed by means of the Mini Mental Status Examination (MMSE) in both patients and HC. All patients underwent a previously reported (Santangelo et al., 2009; Vitale et al., 2011) standardized neuropsychological evaluation to assess frontal
ACCEPTED MANUSCRIPT lobe/executive functions and memory. Patients were screened for ICD by the modified version of MIDI (Weintraub et al., 2006, 2015). It comprises five modules: compulsive buying (CB), pathological gambling (PG), hypersexuality (HS), compulsive eating (CE) and punding. Each module is composed by a screening general question to which the subject has to answer
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affirmatively (1 point) or negatively (0 points). If the screening question is answered in the negative, the interviewer moves to the next module. If it is answered in the affirmative, the
interviewer asks a series of additional questions reflecting DSM-IV criteria and pertaining to
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commonly reported thoughts, urges/desires, and behaviors associated with ICD. Each additional question is rated on a 4-point Likert scale (score 0–3 for each question) to measure the frequency of
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behaviors, thoughts, urges/desires associated with each ICD. In the present study, the total MIDI score was calculated and ranged from 0 to 56 (0-7 for CB; 0-13 for PG; 0-10 for HS; 0-10 for CE; 0-16 for punding), with a higher score indicating greater severity (i.e. frequency) of symptoms. The presence of ICD was defined as answering in the affirmative to 1 (compulsive sexual behavior and
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compulsive shopping) or 2 (compulsive gambling) gateway questions plus an affirmative answer to ≥1 of the remaining questions of the relevant ICD module of the MIDI. Screening procedures were administered to patients and, separately, to their caregivers. Patients with a direct and/or indirect
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report of any ICD during the previous 2 months were considered positive at screening and underwent a specific interview based on the clinical diagnostic criteria for PG and HS. Since
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diagnostic criteria for CE are absent, we followed the definition of this condition reported in Nirenberg and Waters (2006) in which the CE was defined as an uncontrollable consumption of a larger amount of food than normal, in excess of that necessary to alleviate hunger. Moreover, to detect the occurrence of punding, we followed the definition provided by Fernandez and Friedman (1999). The punding is defined as an intense fascination with complex, excessive, repetitive, nongoal oriented behaviors including less complex acts such as shuffling papers, reordering bricks or sorting handbags or more complex acts such as hobbysm, writing or excessive computer use.
ACCEPTED MANUSCRIPT Patients and HC underwent a supplementary neuropsychiatric examination consisting of: (1) the Hamilton Depression Rating Scale (HAM-D) to measure severity of depressive symptoms and to identify clinically significant depression (Hamilton, 1960); (2) the Hospital Anxiety and Depression Scale (HADS), to measure severity of anxiety and depression (Leentjens et al., 2011) and (3) an
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interview based on DSM-IV criteria for hypomanic or manic episodes, psychosis, substance abuse, and ADHD. HC were also screened for the presence of ICD by means of a clinical interview based
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on DSM-IV criteria.
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2.5 Imaging Parameters
All subjects were scanned in the morning and patients were all in the “on-medication” state. Magnetic resonance images were acquired on a 3 T GE scanner equipped with an 8-channel parallel head coil. fMRI data consisted of 240 volumes of a repeated gradient-echo echo planar imaging
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T2*-weighted sequence (TR = 1508 ms, axial slices = 29, matrix = 64 x 64, field of view = 256 mm, thickness = 4 mm, interslice gap = 0mm). During the functional scan, subjects were asked to simply stay motionless, awake, with their eyes closed. Three-dimensional high-resolution T1-
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weighted sagittal images (GE sequence IR-FSPGR, TR = 6988 ms, TI = 1100 ms, TE = 3.9 ms, flip angle = 10, voxel size = 1 x 1 x 1.2 mm3) were acquired for registration and normalization of
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the functional images.
2.6 Statistical analysis of clinical, motor and neuropsychological data Differences in the distribution of categorical variables among groups were assessed by means of chi-square. For the analysis of differences among groups on demographic, clinical, behavioral (HADS and HAM-D) and neuropsychological variables we used non-parametric tests (Kruskal– Wallis H test to compare three samples, and the Mann–Whitney U test to compare two samples) to
ACCEPTED MANUSCRIPT avoid biases due to the small sample size. A p value less than 0.05 was considered statistically significant. Analyses were performed with SPSS version 13 (SPSS Inc. Chicago, IL).
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2.7 Resting-state fMRI pre-processing and statistical analysis Image data pre-processing and statistical analysis were performed with BrainVoyager QX (Brain Innovation BV, The Netherlands). Before statistical analyses, individual functional data were coregistered to their own anatomical data and spatially normalized to the standard Talairach space.
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Single-subject and group-level ICA was carried out respectively with the fastICA (Hyvarinen et al.,
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2001) and the self-organizing group ICA (sogICA) algorithms (Esposito et al., 2005). For each subject, 40 independent components were extracted. All single-subject component maps from all subjects were then “clustered” at the group level, resulting in 40 single-group average maps that were visually inspected to recognize the major physiological resting-state networks. The selection of the clusters of interest implied identifying in each group component map the presence of
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anatomically relevant areas that, jointly and consistently across subjects, reproduced the layouts of the major physiological RSNs.
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The most reported and investigated RSNs are the default mode network (DMN), the left and right central executive networks (CEN), the salience network (SN), the sensorimotor network (SMN) and
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the visual and auditory networks (Damoiseaux et al., 2006). For homologue components corresponding to a given RSN, a multi-subject random effects (RFX) analysis was carried out that treated the individual subject map values as random observations at each voxel. Single-group onesample t-tests were used to map the whole-brain distribution of the components of interest (p=0.05, cluster-level corrected) in each patient group and in the control group. An inclusive mask was created from the single-group component maps to define the search volume for within-network group comparisons (i.e. ICD+ vs ICD-, ICD+ vs HC and ICD- vs HC). Cognitive outcomes, LEDD and age were used as additional covariates in the comparison between ICD+ and ICD- group. Then, a two-sample t-test was computed at each voxel of the mask to produce a t-map of the differences
ACCEPTED MANUSCRIPT that was applied a minimum threshold of p=0.05 (corrected for multiple comparisons). To correct for multiple comparisons, regional effects were only accepted for clusters exceeding a minimum size determined with a non-parametric randomization approach. Namely, an initial voxel-level threshold was set to p=0.01 (uncorrected) and a minimum cluster size was estimated after 1000
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Montecarlo simulations that protected against false positive clusters up to 5%.
Individual ICA z-scores for both the patient groups and the control group were extracted from regions identified in the above analyses and used for all between-group comparisons and for linear
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correlation analyses with MIDI scores and other clinical and neuropsychological variables.
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2.8 VBM analysis
Data were processed and examined using SPM8 software (Wellcome Trust Centre for Neuroimaging, London, UK; http://www.fil.ion.ucl.ac.uk/spm), where we applied VBM implemented in the VBM8 toolbox (http://dbm.neuro.uni-jena.de/vbm.html) with default
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parameters incorporating the DARTEL toolbox, which was used to obtain a high-dimensional normalization protocol (Ashburner, 2007). Images were bias-corrected, tissue-classified, and registered using linear (12-parameter affine) and non-linear transformations (warping) within a
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unified model (Ashburner, 2007). Subsequently, the warped grey matter (GM) segments were affine-transformed into Montreal Neurological Institute (MNI) space and were scaled by the
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Jacobian determinants of the deformations to account for the local compression and stretching that occurs as a consequence of the warping and affine transformation (modulated GM volumes. Finally, the modulated volumes were smoothed with a Gaussian kernel of 8-mm full-width at half maximum (FWHM). The GM volume maps were statistically analyzed using the general linear model based on Gaussian random field theory. Statistical analysis consisted of an ANCOVA with total intracranial volume (TIV), gender, age, neuropsychological data differing between patients with and without ICD and LEDD as covariates of no interest. Statistical inference was performed at the voxel level, with a family-wise error (FWE) correction for multiple comparisons (p<0.05).
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3. Results 3.1 Clinical, behavioral and cognitive findings
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Fifteen patients with PD with ICD (ICD+), 15 patients with PD without ICD (ICD-) and 24 age and sex-matched HC were enrolled in the study.
The clinical and demographic characteristics of patients are shown in Table 1. No significant differences were detected between ICD+ and ICD- patients on the demographic, clinical and LEDD
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variables. As expected, ICD+ patients differed significantly (p<0.05) from ICD- patients on MIDI
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scores, with HS (87%) and CE (54%), alone or combined, most commonly recorded; PG was found as less frequent behavior disorder (7%) in our patients. Moreover, ICD+ compared to ICD- patients performed significantly worse (p<0.05) on neuropsychological tests exploring both executive and memory functions (see Table 2). The severity of affective disorders (HAM-D and HADS) did not differ between PD patient subgroups (see Tables 1-2 for cognitive and behavioral data). No PD
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patient or HC was affected by anxiety disorder, mania, hypomania, or bipolar disorder.
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3.2 Imaging data and correlation analysis
By visually inspecting ICA-derived components of RS-fMRI data for each group (ICD+ and ICD-,
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HC), we identified RSNs components using similar methodology as that of previous studies (Damoiseaux et al., 2006). Significant functional connectivity changes between ICD+ and ICDpatients were identified within the following RSNs (see Fig. 1): 1. SN: ICD+ patients showed increased component time course-related activity compared to ICDpatients in the bilateral insula (right: x=40; y=-6; z=2; left: x=-39; y=-9; z=-1) and right VS (x=9; y=2; z=8); 2. CEN: at the level of the right CEN, ICD+ patients showed decreased component time courserelated activity compared to ICD- patients in the dorsolateral prefrontal cortex (DLPFC, BA 9; x=32; y=-74; z=36) and the inferior parietal cortex (IPL, BA 40; x=44; y=1; z=22).
ACCEPTED MANUSCRIPT 3. DMN: ICD+ patients showed increased component time course-related activity compared to ICD- patients in the left middle temporal gyrus (BA19; x=-51; y=-73; z=25). The comparison between ICD+ and HC (data not shown) revealed the following statistically significant changes: ICD+ patients showed decreased component time course-related activity in the
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anterior cingulate cortex (BA24, x=-3; y=29; z=19) and in the precuneus (x=-15, y=-55, z=25) within the SN and DMN, respectively. Moreover, ICD+ patients showed increased component time course-related activity within the right CEN in the right angular gyrus (x=42, y=-73, z=31).
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The comparison between ICD- and HC (data not shown) revealed the following statistically significant changes: ICD- patients showed decreased component time course-related activity in the
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anterior cingulate cortex (BA24, x=-3; y=29; z=19) within the SN. Within the DMN, ICD- showed decreased component time course-related activity in the left middle temporal gyrus (x=-48, y=-64, z=25) and in the precuneus (x=-15, y=-55, z=25). Moreover, ICD- patients showed increased component time course-related activity within the right CEN in the right angular gyrus (x=42, y=-
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73, z=31).
Correlation analyses revealed that MIDI scores were positively correlated with DMN (R=0.8; p = 0.003) and SN (R=0.6 ; p=0.02) connectivity changes only in the ICD+ group (see Fig. 2). No
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correlation was found between any other clinical, demographic or cognitive measures and imaging data.
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VBM data did not reveal any statistically significant differences in local GM between ICD+ and ICD- patients and between all patients and HC (p<0.05. FWE).
4. Discussion Our imaging data demonstrated that ICD+ patients exhibit a reduced functional brain connectivity within the CEN and an increased connectivity within both SN and DMN compared to ICD- patients. Notably, these functional abnormalities were correlated with ICD severity and were independent
ACCEPTED MANUSCRIPT from motor, cognitive features and dopaminergic load. This divergent pattern may represent a neural correlate of the presence of ICD symptoms in PD patients. SN, CEN and DMN are three large-scale networks that play a pivotal role in cognitive and affective information processing (Sridharan et al., 2008; Bressler and Menon, 2010). The SN is a limbic-
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paralimbic network encompassing cortical and subcortical hubs involved in affective, reward processing and interoceptive awareness, such as the anterior insula, the ACC and the VS (Seeley et al., 2007; Menon and Uddin, 2010). The SN is thought to exert an important function in orienting
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attention to behaviorally salient and rewarding stimuli and facilitating goal-directed behavior (Cai et al., 2016; Menon et al., 2015). The CEN encompasses mainly the PFC and IPL and is critical to
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process external, attentionally driven executive functions as well as for judgment and decisionmaking (Miller and Cohen, 2001; Petrides, 2005; Koechlin and Summerfield, 2007). Finally, the DMN includes the posterior cingulate, medial prefrontal and medial temporal cortices, and is involved in ruminations, mind-wandering, emotional processing and cognitive social functions
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(Buckner et al., 2008; Spreng et al., 2009). Previous functional connectivity studies have demonstrated that CEN and DMN time-courses are usually anti-correlated (Fox al., 2005; Menon, 2011). In response to external stimuli the CEN is activated whereas the DMN is deactivated,
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following an antagonism which seems to be critical to generate and maintain an efficient behavioral and cognitive performance (Menon et al., 2011). The SN has shown to orchestrate a dynamic
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switching between the DMN and CEN, engaging the CEN while disengaging the DMN when an accurate control is needed to effort a cognitive demanding task (Menon and Uddin, 2010; Menon, 2011). During rest, the SN might contribute to maintain the DMN hypoconnected while the CEN is hyperconnected, allowing an individual to remain prepared to unexpected environmental events (Fox et al., 2005). In the present study, the SN showed an increased connectivity in the ICD+ group which was positively correlated with symptoms severity (as assessed by MIDI scores). Both these findings are not surprising, considering the abovementioned role of crucial SN hubs in the reward system. Particularly, the VS may be considered as an interface of cortical and limbic inputs and
ACCEPTED MANUSCRIPT relies on downstream structures to drive reward-related actions (Haber and Knutson, 2010). Indeed, an abnormal activity within this structure has been also found in several task-related imaging studies in PD patients with impulse controls behaviors (Rao et al., 2010). Similarly, the insula has been proposed as the core node acting in detection and selection of salient events, and starting
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additional processing to generate an efficient behavioral response (Singer et al., 2009; Menon and Uddin, 2010). Remarkably, an abnormal connectivity within the SN has been revealed also in many addiction disorders, like substance-related disorders (Qiu et al., 2017), nicotine addiction (Fedota
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and Stein, 2015; Li et al., 2016) and online gaming disorder (Zhang et al., 2016), revealing an impaired connectivity within cortical areas linked to impulsivity and behaviors control. Therefore,
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our finding of an increased connectivity within the SN may itself rely on reward system and decision-making aberration in PD patients with ICD. Furthermore, the presence of a disrupted connectivity within the SN may also lead to an aberrant regulatory role and functional coupling with the DMN and CEN. However, since we did not directly investigate the between-network
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connectivity, our findings cannot support this hypothesis. It is noteworthy that a similar pattern of functional reorganization has been consistently observed in affective disorders (see for a review Mulders et al., 2015) and has been correlated to the presence of an increased ruminative state
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(Greicius et al., 2007). Similarly, we could speculate that the hyperconnected DMN and a reduced CEN connectivity may drive the ICD+ patient to focus on endogenous stimuli. Conversely, external
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stimuli, such as environment and social context, are not considered as prominent anymore whereas the patient is internally-oriented ruminating about his compulsion. Consequently, processing and evaluation of possible outcomes and judging about consequences, which are critical to efficiently drive human behaviors, might be profoundly impaired by the interference of the DMN. Our hypothesis is corroborated by the positive correlations between DMN functional connectivity and ICD symptoms severity (i.e. the more the patient is wandering through self-referential thoughts the more severe are the ICD). On the other hand, decreased connectivity within the CEN may rely on altered risk/benefit evaluation and decision-making. Interestingly, CEN functional connectivity
ACCEPTED MANUSCRIPT and CEN/SN connectivity strength have been shown to protect against relapse to cocaine use following treatment, likely reflecting a better capacity to perform an executive control process when facing reward stimuli induced by cocaine (McHugh et al., 2016). Accordingly, PD patients with ICD are characterized by impaired cognitive flexibility and behavioural planning as evidenced by
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poor performance on Trial making test and other frontal tasks (Santangelo et al., 2009; Vitale et al., 2011, Santangelo et al., 2013). Considering as a whole both neuropsychological and imaging findings, ICD patients would have an impaired ability in selecting, monitoring and shifting from
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disadvantageous alternatives to more advantageous ones, thus perseverating socially and personally non-adaptive behaviors.
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It is noteworthy that a disrupted functional connectivity within SN, DMN, and CEN have been also implicated in other addiction disorders and in abstinence pathophysiology (Pariyadath et al., 2016; Westlye et al., 2016; Qiu et al., 2017). In keeping with these findings the presence of a disrupted connectivity within these three core neurocognitive networks may be considered as a potential
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neural correlate of ICD development in PD patients. Which factor could primarily trigger this functional networks reorganization in ICD patients is still a matter of discussion. A possible explanation could come from several neurobehavioral studies on reward system regulation. Indeed,
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it has been observed that prefrontal inputs on the VS enable the ability to shift behavioral focus as cues salience change (Goto and Grace, 2005; Sesack and Grace, 2010) whereas hippocampal
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connections contribute to keep the individual focused on the task (Maren, 1999; Fanselow, 2000). These functions are regulated by dopamine firing: prefrontal connections are inhibited by dopamine via D2-receptors, whereas hippocampal connections are activated by dopamine via D1-receptors. Therefore, it has been proposed that when dopaminergic system is overload, as it might occur in ICD+ patients under dopaminergic treatment, prefrontal influence on VS may be disabled whereas the hippocampal drive is reinforced (Napier et al., 2015). This condition might make the patients consistently focused on the reward stimuli which have primarily induced the dopamine release, looping the reward-system (Napier et al., 2015). Thus, we hypothesize that this dopaminergic
ACCEPTED MANUSCRIPT overstimulation might underlie the observed increased connectivity within the SN and DMN as well as the decreased connectivity within the CEN. This functional phenomenon is not driven by the dopaminergic treatment load, as no difference has been found in both LEDD and LEDD-DA between the two study groups. In previous PET 11C-raclopride studies (Steeves et al., 2009;
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O’Sullivan et al., 2011), ICD+ patients have been shown to present a greater decrease in binding potential in the VS during reward cues and tasks compared to ICD- patients and controls, likely reflecting a pre-existing “hyperdopaminergic state”. This “hyperdopaminergic state” may affect the
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dopaminergic drive within the reward system, as discussed above. Thus, further investigations are needed to clarify whether predisposing genetic (Lee et al., 2012; Vallelunga et al., 2012),
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enviromental (Voon et al., 2007) or neuropsychological (Antonini et al., 2011) factors may induce this high dopaminergic response to reward stimuli in the cortico-striato-cortical pathway, which may represent a pre-existing vulnerability to eventually develop ICD after dopaminergic replacement therapy.
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The observed connectivity abnormalities were detected in the absence of statistically significant GM changes in the same regions. Moreover, in a previous morphometric study (Tessitore et al., 2016) on the same PD population, we did not find any corticometric change in the same
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functionally disrupted regions. Future studies are needed to better clarify the relationship between functional and structural abnormalities underlying ICD in PD, which can be both influenced by the
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chronic dopaminergic treatment. The present study has some limitations worth noting. First, although our patients subgroups were clinically well matched, our sample size was relatively small. Second, we have not used the questionnaire for impulsive-compulsive disorders in Parkinson's disease rating scale (QUIP-RS) (Weintraub et al., 2012). However, to our knowledge this scale has not been yet validated in Italian and has been developed after our study recruitment initiation. These limitations notwithstanding, our findings provide additional insights into the mechanisms underlying ICD development in PD.
ACCEPTED MANUSCRIPT Future longitudinal studies including larger PD populations with ICD in an early phase of the
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disease are needed to verify these observations.
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ACCEPTED MANUSCRIPT Zhang JT, Yao YW, Li CS, Zang YF, Shen ZJ, Liu L, et al. Altered resting-state functional connectivity of the insula in young adults with Internet gaming disorder. Addict Biol 2016;21:743751.
Figure 1 Resting-state network connectivity changes.
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Figure captions
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Whole-brain significant connectivity differences between ICD+ and ICD- patients (p<0.05
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corrected) within central executive, default-mode and salience networks and bar graphs of the average ICA z-scores (IPL, ICD+: 1.8±1.3; ICD-: 3.1±1.5; p=0.01; DLPFC, ICD+: 0.5±0.5; ICD-: 1.5±1.3; p=0.007; MTG, ICD+: 0.3±1.1; ICD-: -0.4±0.5; p=0.04; VS, ICD+: 0.8±1.5; ICD-: 0.2±0.9; p=0.03; l-INS, ICD+: 1.3±1.3; ICD-: 0.3±0.8; p=0.03; r-INS, ICD+: 0.9±1; ICD-: 0.2±1.1;
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p=0.04).
IPL: inferior parietal; DLPFC: dorsolateral prefrontal cortex; MTG: middle temporal gyrus; VS:
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ventral striatum; l-INS: left insula; r-INS: right insula.
patients.
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Correlation analyses between MIDI scores and SN and DMN average ICA z-scores in ICD+
ACCEPTED MANUSCRIPT Disclosure Statement Authors state that there are no actual or potential conflicts of interest and no relevant financial
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support. All authors have approved the final article.
ACCEPTED MANUSCRIPT Table 1: Demographic, clinical and behavioural features of ICD+ and ICD- patients and HC HC (n=24) mean±SD (mean rank)
ICD+ (n=15) mean±SD (mean rank)
ICD(n=15) mean±SD (mean rank)
Age
63.54±6.7 (28.4)
62.8±8.6 (27.1)
63.1±8 (26.4)
Education
10.3±3.7 (24.6)
9.8±5 (22.1)
12.9±8 (31.9)
17/7
13/2
12/3
HAM-D
7.2±2.7 (25.6)
8.6±4.7 (27.7)
HADS
9.6±3.7 (27.8)
11.3±8.2 (27.5)
n.a
H&Y stage
n.a
UPDRS III
n.a
Total LEDD (mg daily)
MIDI score
- HS - CE - PG
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Frequency of ICD
n.a.
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DA LEDD (mg daily)
n.a
- Co-occurrence of HS and CE
n.a.
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0.1
1.3
0.2
7.4 ±3.9 (24.7)
0.2
0.8
8.1±7.5 (19.7)
3.0
0.2
Mann-Whitney U test
5.3±2.9 (11.9)
6.6±3.9 (15.8)
64.5
0.2
1.3±0.5 (13.5)
1.4±0.6 (14.4)
84.5
0.7
10.9±4.5 (13.3)
12.1±4.4 (14.6)
82.5
0.6
70.5
0.3
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0.9
3.4
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Gender (M/F)
0.1
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Kruskal-Wallis p -value test/χ2
477.3 ±222.9 532.1±207.2 (12.4) (15.4) 243.3±82.1 (12.5)
243.3±90.2 (15.3)
73.0
0.3
6.8±3.2 (21.5)
0.4±0.5 (7.5)
0.0
<0.001
n 6 4 1 4
H&Y stage: Hoehn & Yahr stage; UPDRS: Unified Parkinson’s Disease Rating Scale; HAM-D: Hamilton Depression Rating Scale; HADS: Hospital Anxiety and Depression Scale; MIDI: Minnesota Impulsive Disorders Interview; LEDD: Levodopa Equivalent Daily Dose; DA: dopamine agonist; HS: Hypersexuality; CE: Compulsive Eating; PG: Pathological Gambling; n.a. = not available; χ2=chi-squared. The p values of post-hoc analysis (by Mann-Whitney U test) for comparison between HC and ICD+ are the following: 0.743 for age; 0.582 for education; 0.254 for gender; 0.718 for HAM-D; 0.960 for HADS. The p values of post-hoc analysis (by Mann-Whitney U test) for comparison between HC and ICD- are the following: 0.731 for age; 0.135 for education; 0.524 for gender; 0.846 for HAM-D; 0.082 for HADS. The p values of post-hoc analysis (by Mann-Whitney U test) for comparison between ICD+ and ICD- are the following: 0.914 for age; 0.116 for education; 0.624 for gender; 0.583 for HAM-D; 0.231 for HADS.
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Significant differences were reported in bold.
ACCEPTED MANUSCRIPT Table 2: Cognitive evaluation of PD patients and HC
28.2±1.8 n.a. n.a.
Corsi’s test
n.a.
WCST-global score
n.a.
ROCF-copy task
n.a.
RCPM
n.a.
VF
n.a.
SF
n.a.
TMT:B
n.a.
Interference task of Stroop Test Attentional matrices
n.a.
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Verbal delayed recall
n.a.
ICD(n=15) mean±SD (mean rank) 27.1±1.8 43.8±11.4 (17.25) 9±3 (16.65) 5.1±0.9 (15.86) 82.1±30.3 (11.07) 29.2±4.8 (15.71) 28.7±5.5 (17.39) 37.5 ± 10.4 (17.75) 20 ± 4.3 (17.93) 119.2 ± 68.5 (10.86) 13.9 ± 7.5 (15.89) 51.5 ± 6.4 (16.11)
KruskalWallis/MannWhitney test
p-value
75.5 45.5
0.452 0.027
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MMSE Verbal immediate recall
ICD+ (n=15) mean±SD (mean rank) 26.5±2.2 33.6±7.2 (10.50) 6.3±2.6 (10.35) 4.5±0.9 (12.0) 101.4±34.3 (17.15) 24.6±8.9 (12.15) 22.2±7.9 (10.35) 24.5 ± 13.4 (9.96) 15.1 ± 5.7 (9.77) 181.1 ± 98.6 (17.38) 10.5 ± 7.8 (11.96) 45.8 ± 11.3 (11.73)
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HC (n=24) mean±SD
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Cognitive Tasks
43.5
0.034
65.0
0.220
50.0
0.045
62.5
0.165
43.5
0.021
38.5
0.009
36.0
0.007
47.0
0.033
64.5
0.197
61.5
0.151
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MMSE: Mini Mental State Examination; WCST: Wisconsin Card Sorting Test; ROCF: Rey-Osterrieth Complex Figure Test; RCPM: Raven’s 47 Coloured Progressive Matrices; VF: Phonological verbal fluency; SF: Semantic Fluency; TMT-B: part B of the Trail Making Test. The p values of post-hoc analysis (by Mann-Whitney U test) on MMSE were the following: 0.005 for comparison between HC and ICD+; 0.067 for comparison between HC and ICD-; 0.259 for comparison between ICD+ and ICD-. Significant differences were reported in bold.
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ACCEPTED MANUSCRIPT Highlights We evaluated resting-state functional connectivity changes in PD patients with ICD.
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ICD+ patients exhibit a disrupted connectivity within DMN, SN and CEN networks.
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ICD severity was associated with functional connectivity changes within DMN and SN.
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We confirm the role of the prefrontal-limbic-striatal pathway in ICD development.
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