J Neurol (2015) 262:425–434 DOI 10.1007/s00415-014-7591-5
ORIGINAL COMMUNICATION
Resting-state functional connectivity associated with mild cognitive impairment in Parkinson’s disease Marianna Amboni • Alessandro Tessitore • Fabrizio Esposito • Gabriella Santangelo Marina Picillo • Carmine Vitale • Alfonso Giordano • Roberto Erro • Rosa de Micco • Daniele Corbo • Gioacchino Tedeschi • Paolo Barone
•
Received: 17 September 2014 / Revised: 31 October 2014 / Accepted: 15 November 2014 / Published online: 27 November 2014 Ó Springer-Verlag Berlin Heidelberg 2014
Abstract Cognitive impairment is common in PD, even in early stages. The construct of mild cognitive impairment has been used to identify clinically evident cognitive impairment without functional decline in PD patients (PDMCI). The aim of the present study was to investigate brain connectivity associated with PD-MCI through RS-fMRI. RS-fMRI at 3T was collected in 42 PD patients and 20 matched healthy controls. Among PD patients, 21 were classified as having MCI (PD-MCI) and 21 as cognitively unimpaired (PD-nMCI) based on criteria for possible PDMCI (level I category). Single-subject and group-level ICA
M. Amboni and A. Tessitore contributed equally to the manuscript. M. Amboni F. Esposito M. Picillo R. Erro P. Barone (&) Neurodegenerative Diseases Center, Department of Medicine and Surgery, University of Salerno, Via Allende, 84081 Baronissi, SA, Italy e-mail: [email protected] M. Amboni C. Vitale A. Giordano P. Barone Istituto di Diagnosi e Cura Hermitage-Capodimonte, Naples, Italy A. Tessitore A. Giordano R. de Micco D. Corbo G. Tedeschi Department of Neurology, Second University of Naples, Naples, Italy F. Esposito Department of Cognitive Neuroscience, Maastricht University, Maastricht, The Netherlands G. Santangelo Neuropsychology Laboratory, Department of Psychology, Second University of Naples, Caserta, Italy C. Vitale University Parthenope, Naples, Italy
was used to investigate the integrity of brain networks related to cognition in PD patients with and without MCI. Image data processing and statistical analysis were performed in BrainVoyager QX. In addition, we used VBM to test whether functional connectivity differences were related to structural abnormalities. PD-nMCI and PD-MCI patients compared with controls showed decreased DMN connectivity. PD-MCI patients, but not PD-nMCI, compared with controls, showed decreased functional connectivity of bilateral prefrontal cortex within the frontoparietal network. The decreased prefrontal cortex connectivity correlated with cognitive parameters but not with clinical variables. VBM analysis did not reveal any difference in local gray matter between patients and controls. Our findings suggest that an altered DMN connectivity characterizes PD patients, regardless of cognitive status, whereas a functional disconnection of the frontoparietal network could be associated with MCI in PD in the absence of detectable structural changes. Keywords Movement disorders Parkinson’s disease Cognitive disorders and dementia Mild cognitive impairment fMRI Imaging
Introduction Cognitive impairment is common in PD, even in early stages. Executive, attention, memory and visuospatial functions have been shown as the cognitive domains more frequently affected [1, 2]. The construct of mild cognitive impairment in PD (PDMCI) has been used to identify clinically evident cognitive impairment without functional decline [3, 4] but associated with higher risk to develop PD dementia (PDD) [5]. Since
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PDD has been associated with increased disability, hospitalization [6] and ultimately higher mortality [7], recognizing PD-MCI and its biological markers would be crucial to identify a PD subpopulation at higher risk of worse disease progression. Previous structural imaging studies have associated PDMCI with limited areas of cortical atrophy in frontal, temporal and parietal lobes [8–11]. With regard to metabolic studies on PD-MCI, the most consistent findings have been both prefrontal and parieto-occipital cortices hypometabolism [12, 13] even in newly untreated PD patients [14]. Functional resonance neuroimaging biomarkers are particularly appealing since they are non-invasive techniques able to detect abnormalities prior to widespread atrophy; in addition, RS-fMRI is a cognitively unbiased approach for exploring functional brain connectivity [15]. In combination with ICA [16], RS-fMRI allows to characterize the spatio-temporal distribution of spontaneous coherent fluctuations of blood oxygenation level-dependent (BOLD) signals between temporally correlated brain regions (i.e., RS networks). The DMN and the frontoparietal networks (FPNs) are thought to be most relevant for cognition [17, 18]. Recent RS- and task-related fMRI studies have demonstrated decreased DMN connectivity in cognitively unimpaired PD patients [19–21] and a selective disruption of corticostriatal connectivity in bilateral prefrontal regions in a cohort of PDD patients [22]. Moreover, previous seed-based RS-fMRI studies have shown a decreased DMN and visual network (VN) connectivity in patients with PDD [23, 24]. To our knowledge, only one study has investigated resting-state brain connectivity in patients with PD and MCI [25]. Therefore, the aim of the present study was to evaluate functional brain connectivity changes associated with PD-MCI using RS-fMRI to assess whether specific connectivity patterns characterize PD-MCI compared to those without MCI (PD-nMCI) and to healthy subjects (HS). In addition, we used voxel-based morphometry (VBM) to assess whether between-group differences in RS functional connectivity were dependent on structural abnormalities.
J Neurol (2015) 262:425–434
Study population Patients were screened from a series of consecutive outpatients at the Movement Disorders Unit of the University of Naples ‘‘Federico II’’, with a diagnosis of PD according to UK Parkinson’s Disease Society Brain Bank Diagnostic Criteria [26]. Inclusion criteria for screening were: (1) PD onset after the age of 40 years to exclude early onset parkinsonism; (2) Hoehn & Yahr (H&Y) stage ranging from 1 to 2 while in ‘‘on state’’; and (3) antiparkinsonian treatment at a stable and optimized daily dosage during the 4 weeks prior to study entry. Exclusion criteria were: (1) PDD [27]; (2) major depression, minor depression, dysthymic disorder according to DSM-IV criteria [28]; (3) clinically significant or unstable medical condition including serious cardiovascular or cerebrovascular disease; (4) any treatment potentially interfering with cognition including psychotropic agents and anticholinergic drugs. Among screened patients, 21 PD-MCI patients were enrolled. PD-MCI classification was based on the presence of the following two conditions that had both to be fulfilled: (1) a clinically evident cognitive deficit (reported by either the patient or informant, or observed by the clinician) not causing a significant functional decline; and (2) impairment on at least two neuropsychological tests, as demonstrated by performance being at least 1.5 standard deviation below the expected age and education-corrected mean score in the specific neuropsychological tests [4]. A group of 21 PD patients not fulfilling MCI criteria (PDnMCI) and matched for age, gender and disease duration to PD-MCI group was also enrolled. HS were enrolled among volunteers, age- and gendermatched to the patients’ population. Exclusion criteria for healthy subjects were: (1) dementia according to DSM-IV criteria [28] or MCI according to the Petersen criteria [29]; (2) clinically significant diseases including neurologic disorders and cardiovascular/respiratory diseases; (3) major depression, minor depression, dysthymic disorder according to DSM-IV criteria [28]. In particular, MCI was detected by means of a semistructured interview aimed at identifying any memory and/or other cognitive deficit complaint (core criterion for MCI) [29].
Methods
Clinical and cognitive evaluation
Ethics statement
All patients were evaluated 60–90 min after their morning dose of dopaminergic drugs. Disease severity was assessed by means of the H&Y stage and the Unified Parkinson’s Disease Rating Scale (UPDRS). All patients were evaluated using an abbreviated neuropsychological battery aimed at identifying PD-MCI
All participants provided written informed consent to the protocol that has been performed in accordance with the Declaration of Helsinki and approved by the local Ethics Committee.
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J Neurol (2015) 262:425–434
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(level I category) The neuropsychological battery included the following tests: (1) for episodic memory: the Rey auditory 15-word learning test, immediate recall and delayed recall [30]; (2) for the executive/attentional domain: the Frontal Assessment Battery (FAB) [31], the Stroop color-word test [32] and the attentional matrices [33]; and (3) for the visuospatial domain: the Constructional apraxia test (CA) [33] or the copy task of Rey– Osterrieth Complex Figure test [34], and the Clock test (CT) [35]. Tests scores were corrected for current normative values. HS were assessed with Mini Mental State Examination (MMSE).
PD-MCI and PD-nMCI were compared by means of Mann–Whitney U test. Scores for each cognitive test were z scores transformed, according to methods reported elsewhere [36]. Performance on each cognitive domain for each subject was expressed by an aggregate z score obtained by averaging the standardized scores of the tests loading on that cognitive domain (i.e., memory, executive/ attention, visuospatial). A cognitive domain was considered altered when its aggregate z score resulted at least 1.5 standard deviation below the expected age and educationcorrected mean z score of that specific cognitive domain [36]. Computation was supported by the Statistical Package for the Social Sciences (SPSS 16.0).
Imaging parameters
Resting-state fMRI analysis
MRIs were acquired on a 3T General Electric Medical System scanner equipped with an 8-channel parallel head coil. FMRI data consisted of 240 volumes of a repeated gradient-echo echoplanar imaging T2*-weighted sequence [repetition time (TR) = 1.508 ms, axial slices = 29, matrix = 64 9 64, field of view = 256 mm, thickness = 4 mm, interslice gap = 0 mm). During the functional scan (6 min), subjects were asked to stay motionless and awake with their eyes closed. Three-dimensional highresolution T1-weighted sagittal images (GE sequence IRFSPGR, TR = 6.988 ms, inversion time = 1.100 ms, echo time = 3.9 ms, flip angle = 10, voxel size = 1 9 1 9 1.2 mm3) were acquired for registration and normalization of the functional images as well as for atrophy measures and VBM analysis. A T2-fluid-attenuated inversion recovery (T2-FLAIR) sequence [TR 9.000 ms, TE 1.200 ms, inversion time (TI) 2.500, axial slices 44, matrix 224 448, FOV 240 mm, thickness 3 mm, interslice gap 0 mm] was also acquired in all subjects and the MIPAV software (Medical Image Processing, Analysis and Visualization; version 4.2.2; http://mipav.cit.nih.gov) was used to contour lesions and compute the white matter hyperintensities (WMH) load for each subject. All patients were assessed during on state (60–90 min after their morning dose of dopaminergic drugs).
Image data 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 anatomic data and spatially normalized to the standard Talairach space. Nuisance signals (global signal, white matter and cerebrospinal fluid signals and motion parameters) were regressed out from each data set. Motion parameter estimates were carefully checked for each individual and each group separately: in each individual, head motion did not exceed the voxel size in any directions and there were no significant group differences between groups. Single-subject and group-level ICA was carried out, respectively, with the fast ICA [37] and the self-organizing group ICA [38] algorithms. For each subject, 40 independent components (corresponding to one-sixth of the number of time points; see, e.g., Greicius et al. [39]) were extracted. All singlesubject component maps from all subjects were then ‘‘clustered’’ at the group level using the spatial correlation as similarity measure [38], resulting in 40 single-group average maps that were visually inspected to recognize the main physiologic RS networks, and particularly, the DMN, the FPN and the VN. The sign-adjusted ICA components of all subjects were then submitted to a second-level multisubject random-effects analysis that treated the individual subject map values as random observations at each voxel [16]. Sign adjustment was needed before pooling ICA components from multiple subjects, because ICA decomposition is defined up to a scaling, a permutation and a sign factor [40]. Single-group one-sample t tests were used to analyze the whole-brain distribution of the DMN, FPN and VN components in each group separately and the resulting t maps were thresholded at p = 0.05 (Bonferroni corrected over the entire brain). An inclusive mask was also created from the union of the three single-group maps (PD-nMCI, PD-MCI and HS) and used to define a new search volume for within-network three-group comparisons. To correct for
Statistical analysis of clinical, motor and neuropsychological data Differences in the distribution of categorical variables among groups were assessed by the Fisher’s exact test. Given the small number of subjects in each group and because the distribution of variables could not be assumed to be approximately normal, all statistical analyses applied were nonparametric tests. Demographic variables of PDMCI, PD-nMCI patients and HS were compared using the Kruskal–Wallis test. Clinical and cognitive variables of
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J Neurol (2015) 262:425–434
multiple comparisons, regional effects were only accepted for clusters exceeding a minimum size determined with a nonparametric randomization approach. Namely, an initial voxel-level threshold was set to p \ 0.01 (uncorrected) and a minimum cluster size was estimated after 1,000 Monte Carlo simulations that protected against false-positive clusters up to 5 % [41]. Besides whole-brain analyses, statistical inference was also performed on regions of interest (ROI) that were based on this as well as on our previous study in different cognitively unimpaired PD patients [20]. For these regions, we considered all voxels that were previously found to be significantly different between PD patients and HS, and used these to select and average individual ICA z score data in the present sample of patients. The ICA z score data used for second-level statistics were analyzed as in conventional analyses, i.e., with covariates for the age and gender of subjects. Finally, average individual ICA z scores for both groups were also extracted from regions identified in the above voxel-based analyses and used for regional correlation analyses with individual cognitive domain z scores by means of Spearman’s rho.
bias-corrected, tissue-classified, and registered using linear (12-parameter affine) and nonlinear transformations (warping) within a unified model [42]. Subsequently, the warped gray matter (GM) segments were affine-transformed into Montreal Neurological Institute space and were scaled by the 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. The GM volume maps were statistically analyzed using the general linear model based on Gaussian random field theory. Statistical analysis consisted of an analysis of covariance with total intracranial volume (TIV), age and gender as covariates of no interest. We assessed regional differences, as well as differences over ROI based on the results of the wholebrain RS-fMRI analyses. Statistical inference was performed at the voxel level, with a family-wise error (FWE) correction for multiple comparisons (p \ 0.05).
Results VBM analysis Clinical, motor and neuropsychological evaluation Data were processed and examined using SPM8 software (Wellcome Trust Center for Neuroimaging, London, UK; http://www.fil.ion.ucl.ac.uk/spm), where we applied VBM implemented in the VBM8 toolbox (http://dbm.neuro.unijena.de/vbm.html) with default parameters incorporating the DARTEL toolbox, which was used to obtain a highdimensional normalization protocol [41]. Images were
Sixty-two subjects were enrolled: 42 PD patients and 20 HS. All subjects were right handed. Among PD patients, 21 were classified as having MCI (PD-MCI) and 21 were classified as cognitively unimpaired (PD-nMCI). The three groups did not differ on demographic variables. PD-MCI and PD-nMCI patients did not differ in any of the clinical
Table 1 Demographic and clinical features of PD patients with (PD-MCI) and without Mild Cognitive Impairment (PD-nMCI) and healthy subjects (HS) Demographic and clinical variables
PD-MCI (n = 21)
PD-nMCI (n = 21)
HS (n = 20)
p
Age (years)
65.2 ± 8.7
65.8 ± 6.5
61.9 ± 9.2
0.28
Gender (F/M)
3/18
7/14
8/12
0.17
Education (years)
9.14 ± 4.86
11.04 ± 4.85
10.90 ± 4.36
0.34
c
MMSE
26.68 ± 1.79
28.66 ± 1.43
28.74 ± 0.93
0.0001
Disease duration (years) H & Y stage
6.6 ± 3.7 1.5 ± 0.6
5.9 ± 2.6 1.5 ± 0.4
NA NA
0.61 0.91
14.3 ± 8.5
13.1 ± 5.3
NA
0.98 0.36
UPDRS III Daily levodopa dose (mg daily)
a
453.12 ± 307.39
530.88 ± 299.42
NA
DA agonists dose equivalent to levodopa dosageb
294.74 ± 124.60
285.52 ± 114.05
NA
0.78
WHM load (ml)
0.42 ± 0.43 (0–1.1)
0.40 ± 0.46 (0–0.78)
0.38 ± 0.43 (0–1.2)
0.87
MMSE MiniMental State Examination, H&Y stage Hoehn & Yahr stage, UPDRS Unified Parkinson’s Disease Rating Scale, WHM white matter hyperintensities a b c
16/21 PD-MCI patients and 17/21 PD-nMCI patients were treated with levodopa 9/21 PD-MCI patients and 19/21 PD-nMCI patients were treated with dopamine agonists at commonly used dosage Fisher’s exact test
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Table 2 Data (mean ± standard deviation) from cognitive testing in PD patients with (PD-MCI) and without mild cognitive impairment (PD-nMCI) and between-groups comparisons Cognitive domain
PD-MCI
Episodic memory domain Rey 15 words, 35.07 ± 6.28 immediate recall Rey 15 words, delayed recall
7.27 ± 2.78
PD-nMCI
p
41.31 ± 7.95
0.01
8.52 ± 2.33
NS
and motor variables (Table 1). As expected, the two groups differed significantly on several tests loading on memory, attention/executive, and visuospatial domains (Table 2). Among PD-MCI patients, 11 were impaired in one cognitive domain (all visuospatial), 9 patients showed dysfunction in 2 cognitive domains (7 visuospatial/attention-executive and 2 visuospatial/memory), and 1 patient resulted impaired in 3 cognitive domains (visuospatial/ attention-executive/memory).
Executive/attentional domain Frontal assessment battery
12.55 ± 2.03
15.41 ± 1.73
ORIGINAL COMMUNICATION
Resting-state functional connectivity associated with mild cognitive impairment in Parkinson’s disease Marianna Amboni • Alessandro Tessitore • Fabrizio Esposito • Gabriella Santangelo Marina Picillo • Carmine Vitale • Alfonso Giordano • Roberto Erro • Rosa de Micco • Daniele Corbo • Gioacchino Tedeschi • Paolo Barone
•
Received: 17 September 2014 / Revised: 31 October 2014 / Accepted: 15 November 2014 / Published online: 27 November 2014 Ó Springer-Verlag Berlin Heidelberg 2014
Abstract Cognitive impairment is common in PD, even in early stages. The construct of mild cognitive impairment has been used to identify clinically evident cognitive impairment without functional decline in PD patients (PDMCI). The aim of the present study was to investigate brain connectivity associated with PD-MCI through RS-fMRI. RS-fMRI at 3T was collected in 42 PD patients and 20 matched healthy controls. Among PD patients, 21 were classified as having MCI (PD-MCI) and 21 as cognitively unimpaired (PD-nMCI) based on criteria for possible PDMCI (level I category). Single-subject and group-level ICA
M. Amboni and A. Tessitore contributed equally to the manuscript. M. Amboni F. Esposito M. Picillo R. Erro P. Barone (&) Neurodegenerative Diseases Center, Department of Medicine and Surgery, University of Salerno, Via Allende, 84081 Baronissi, SA, Italy e-mail: [email protected] M. Amboni C. Vitale A. Giordano P. Barone Istituto di Diagnosi e Cura Hermitage-Capodimonte, Naples, Italy A. Tessitore A. Giordano R. de Micco D. Corbo G. Tedeschi Department of Neurology, Second University of Naples, Naples, Italy F. Esposito Department of Cognitive Neuroscience, Maastricht University, Maastricht, The Netherlands G. Santangelo Neuropsychology Laboratory, Department of Psychology, Second University of Naples, Caserta, Italy C. Vitale University Parthenope, Naples, Italy
was used to investigate the integrity of brain networks related to cognition in PD patients with and without MCI. Image data processing and statistical analysis were performed in BrainVoyager QX. In addition, we used VBM to test whether functional connectivity differences were related to structural abnormalities. PD-nMCI and PD-MCI patients compared with controls showed decreased DMN connectivity. PD-MCI patients, but not PD-nMCI, compared with controls, showed decreased functional connectivity of bilateral prefrontal cortex within the frontoparietal network. The decreased prefrontal cortex connectivity correlated with cognitive parameters but not with clinical variables. VBM analysis did not reveal any difference in local gray matter between patients and controls. Our findings suggest that an altered DMN connectivity characterizes PD patients, regardless of cognitive status, whereas a functional disconnection of the frontoparietal network could be associated with MCI in PD in the absence of detectable structural changes. Keywords Movement disorders Parkinson’s disease Cognitive disorders and dementia Mild cognitive impairment fMRI Imaging
Introduction Cognitive impairment is common in PD, even in early stages. Executive, attention, memory and visuospatial functions have been shown as the cognitive domains more frequently affected [1, 2]. The construct of mild cognitive impairment in PD (PDMCI) has been used to identify clinically evident cognitive impairment without functional decline [3, 4] but associated with higher risk to develop PD dementia (PDD) [5]. Since
123
426
PDD has been associated with increased disability, hospitalization [6] and ultimately higher mortality [7], recognizing PD-MCI and its biological markers would be crucial to identify a PD subpopulation at higher risk of worse disease progression. Previous structural imaging studies have associated PDMCI with limited areas of cortical atrophy in frontal, temporal and parietal lobes [8–11]. With regard to metabolic studies on PD-MCI, the most consistent findings have been both prefrontal and parieto-occipital cortices hypometabolism [12, 13] even in newly untreated PD patients [14]. Functional resonance neuroimaging biomarkers are particularly appealing since they are non-invasive techniques able to detect abnormalities prior to widespread atrophy; in addition, RS-fMRI is a cognitively unbiased approach for exploring functional brain connectivity [15]. In combination with ICA [16], RS-fMRI allows to characterize the spatio-temporal distribution of spontaneous coherent fluctuations of blood oxygenation level-dependent (BOLD) signals between temporally correlated brain regions (i.e., RS networks). The DMN and the frontoparietal networks (FPNs) are thought to be most relevant for cognition [17, 18]. Recent RS- and task-related fMRI studies have demonstrated decreased DMN connectivity in cognitively unimpaired PD patients [19–21] and a selective disruption of corticostriatal connectivity in bilateral prefrontal regions in a cohort of PDD patients [22]. Moreover, previous seed-based RS-fMRI studies have shown a decreased DMN and visual network (VN) connectivity in patients with PDD [23, 24]. To our knowledge, only one study has investigated resting-state brain connectivity in patients with PD and MCI [25]. Therefore, the aim of the present study was to evaluate functional brain connectivity changes associated with PD-MCI using RS-fMRI to assess whether specific connectivity patterns characterize PD-MCI compared to those without MCI (PD-nMCI) and to healthy subjects (HS). In addition, we used voxel-based morphometry (VBM) to assess whether between-group differences in RS functional connectivity were dependent on structural abnormalities.
J Neurol (2015) 262:425–434
Study population Patients were screened from a series of consecutive outpatients at the Movement Disorders Unit of the University of Naples ‘‘Federico II’’, with a diagnosis of PD according to UK Parkinson’s Disease Society Brain Bank Diagnostic Criteria [26]. Inclusion criteria for screening were: (1) PD onset after the age of 40 years to exclude early onset parkinsonism; (2) Hoehn & Yahr (H&Y) stage ranging from 1 to 2 while in ‘‘on state’’; and (3) antiparkinsonian treatment at a stable and optimized daily dosage during the 4 weeks prior to study entry. Exclusion criteria were: (1) PDD [27]; (2) major depression, minor depression, dysthymic disorder according to DSM-IV criteria [28]; (3) clinically significant or unstable medical condition including serious cardiovascular or cerebrovascular disease; (4) any treatment potentially interfering with cognition including psychotropic agents and anticholinergic drugs. Among screened patients, 21 PD-MCI patients were enrolled. PD-MCI classification was based on the presence of the following two conditions that had both to be fulfilled: (1) a clinically evident cognitive deficit (reported by either the patient or informant, or observed by the clinician) not causing a significant functional decline; and (2) impairment on at least two neuropsychological tests, as demonstrated by performance being at least 1.5 standard deviation below the expected age and education-corrected mean score in the specific neuropsychological tests [4]. A group of 21 PD patients not fulfilling MCI criteria (PDnMCI) and matched for age, gender and disease duration to PD-MCI group was also enrolled. HS were enrolled among volunteers, age- and gendermatched to the patients’ population. Exclusion criteria for healthy subjects were: (1) dementia according to DSM-IV criteria [28] or MCI according to the Petersen criteria [29]; (2) clinically significant diseases including neurologic disorders and cardiovascular/respiratory diseases; (3) major depression, minor depression, dysthymic disorder according to DSM-IV criteria [28]. In particular, MCI was detected by means of a semistructured interview aimed at identifying any memory and/or other cognitive deficit complaint (core criterion for MCI) [29].
Methods
Clinical and cognitive evaluation
Ethics statement
All patients were evaluated 60–90 min after their morning dose of dopaminergic drugs. Disease severity was assessed by means of the H&Y stage and the Unified Parkinson’s Disease Rating Scale (UPDRS). All patients were evaluated using an abbreviated neuropsychological battery aimed at identifying PD-MCI
All participants provided written informed consent to the protocol that has been performed in accordance with the Declaration of Helsinki and approved by the local Ethics Committee.
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J Neurol (2015) 262:425–434
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(level I category) The neuropsychological battery included the following tests: (1) for episodic memory: the Rey auditory 15-word learning test, immediate recall and delayed recall [30]; (2) for the executive/attentional domain: the Frontal Assessment Battery (FAB) [31], the Stroop color-word test [32] and the attentional matrices [33]; and (3) for the visuospatial domain: the Constructional apraxia test (CA) [33] or the copy task of Rey– Osterrieth Complex Figure test [34], and the Clock test (CT) [35]. Tests scores were corrected for current normative values. HS were assessed with Mini Mental State Examination (MMSE).
PD-MCI and PD-nMCI were compared by means of Mann–Whitney U test. Scores for each cognitive test were z scores transformed, according to methods reported elsewhere [36]. Performance on each cognitive domain for each subject was expressed by an aggregate z score obtained by averaging the standardized scores of the tests loading on that cognitive domain (i.e., memory, executive/ attention, visuospatial). A cognitive domain was considered altered when its aggregate z score resulted at least 1.5 standard deviation below the expected age and educationcorrected mean z score of that specific cognitive domain [36]. Computation was supported by the Statistical Package for the Social Sciences (SPSS 16.0).
Imaging parameters
Resting-state fMRI analysis
MRIs were acquired on a 3T General Electric Medical System scanner equipped with an 8-channel parallel head coil. FMRI data consisted of 240 volumes of a repeated gradient-echo echoplanar imaging T2*-weighted sequence [repetition time (TR) = 1.508 ms, axial slices = 29, matrix = 64 9 64, field of view = 256 mm, thickness = 4 mm, interslice gap = 0 mm). During the functional scan (6 min), subjects were asked to stay motionless and awake with their eyes closed. Three-dimensional highresolution T1-weighted sagittal images (GE sequence IRFSPGR, TR = 6.988 ms, inversion time = 1.100 ms, echo time = 3.9 ms, flip angle = 10, voxel size = 1 9 1 9 1.2 mm3) were acquired for registration and normalization of the functional images as well as for atrophy measures and VBM analysis. A T2-fluid-attenuated inversion recovery (T2-FLAIR) sequence [TR 9.000 ms, TE 1.200 ms, inversion time (TI) 2.500, axial slices 44, matrix 224 448, FOV 240 mm, thickness 3 mm, interslice gap 0 mm] was also acquired in all subjects and the MIPAV software (Medical Image Processing, Analysis and Visualization; version 4.2.2; http://mipav.cit.nih.gov) was used to contour lesions and compute the white matter hyperintensities (WMH) load for each subject. All patients were assessed during on state (60–90 min after their morning dose of dopaminergic drugs).
Image data 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 anatomic data and spatially normalized to the standard Talairach space. Nuisance signals (global signal, white matter and cerebrospinal fluid signals and motion parameters) were regressed out from each data set. Motion parameter estimates were carefully checked for each individual and each group separately: in each individual, head motion did not exceed the voxel size in any directions and there were no significant group differences between groups. Single-subject and group-level ICA was carried out, respectively, with the fast ICA [37] and the self-organizing group ICA [38] algorithms. For each subject, 40 independent components (corresponding to one-sixth of the number of time points; see, e.g., Greicius et al. [39]) were extracted. All singlesubject component maps from all subjects were then ‘‘clustered’’ at the group level using the spatial correlation as similarity measure [38], resulting in 40 single-group average maps that were visually inspected to recognize the main physiologic RS networks, and particularly, the DMN, the FPN and the VN. The sign-adjusted ICA components of all subjects were then submitted to a second-level multisubject random-effects analysis that treated the individual subject map values as random observations at each voxel [16]. Sign adjustment was needed before pooling ICA components from multiple subjects, because ICA decomposition is defined up to a scaling, a permutation and a sign factor [40]. Single-group one-sample t tests were used to analyze the whole-brain distribution of the DMN, FPN and VN components in each group separately and the resulting t maps were thresholded at p = 0.05 (Bonferroni corrected over the entire brain). An inclusive mask was also created from the union of the three single-group maps (PD-nMCI, PD-MCI and HS) and used to define a new search volume for within-network three-group comparisons. To correct for
Statistical analysis of clinical, motor and neuropsychological data Differences in the distribution of categorical variables among groups were assessed by the Fisher’s exact test. Given the small number of subjects in each group and because the distribution of variables could not be assumed to be approximately normal, all statistical analyses applied were nonparametric tests. Demographic variables of PDMCI, PD-nMCI patients and HS were compared using the Kruskal–Wallis test. Clinical and cognitive variables of
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J Neurol (2015) 262:425–434
multiple comparisons, regional effects were only accepted for clusters exceeding a minimum size determined with a nonparametric randomization approach. Namely, an initial voxel-level threshold was set to p \ 0.01 (uncorrected) and a minimum cluster size was estimated after 1,000 Monte Carlo simulations that protected against false-positive clusters up to 5 % [41]. Besides whole-brain analyses, statistical inference was also performed on regions of interest (ROI) that were based on this as well as on our previous study in different cognitively unimpaired PD patients [20]. For these regions, we considered all voxels that were previously found to be significantly different between PD patients and HS, and used these to select and average individual ICA z score data in the present sample of patients. The ICA z score data used for second-level statistics were analyzed as in conventional analyses, i.e., with covariates for the age and gender of subjects. Finally, average individual ICA z scores for both groups were also extracted from regions identified in the above voxel-based analyses and used for regional correlation analyses with individual cognitive domain z scores by means of Spearman’s rho.
bias-corrected, tissue-classified, and registered using linear (12-parameter affine) and nonlinear transformations (warping) within a unified model [42]. Subsequently, the warped gray matter (GM) segments were affine-transformed into Montreal Neurological Institute space and were scaled by the 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. The GM volume maps were statistically analyzed using the general linear model based on Gaussian random field theory. Statistical analysis consisted of an analysis of covariance with total intracranial volume (TIV), age and gender as covariates of no interest. We assessed regional differences, as well as differences over ROI based on the results of the wholebrain RS-fMRI analyses. Statistical inference was performed at the voxel level, with a family-wise error (FWE) correction for multiple comparisons (p \ 0.05).
Results VBM analysis Clinical, motor and neuropsychological evaluation Data were processed and examined using SPM8 software (Wellcome Trust Center for Neuroimaging, London, UK; http://www.fil.ion.ucl.ac.uk/spm), where we applied VBM implemented in the VBM8 toolbox (http://dbm.neuro.unijena.de/vbm.html) with default parameters incorporating the DARTEL toolbox, which was used to obtain a highdimensional normalization protocol [41]. Images were
Sixty-two subjects were enrolled: 42 PD patients and 20 HS. All subjects were right handed. Among PD patients, 21 were classified as having MCI (PD-MCI) and 21 were classified as cognitively unimpaired (PD-nMCI). The three groups did not differ on demographic variables. PD-MCI and PD-nMCI patients did not differ in any of the clinical
Table 1 Demographic and clinical features of PD patients with (PD-MCI) and without Mild Cognitive Impairment (PD-nMCI) and healthy subjects (HS) Demographic and clinical variables
PD-MCI (n = 21)
PD-nMCI (n = 21)
HS (n = 20)
p
Age (years)
65.2 ± 8.7
65.8 ± 6.5
61.9 ± 9.2
0.28
Gender (F/M)
3/18
7/14
8/12
0.17
Education (years)
9.14 ± 4.86
11.04 ± 4.85
10.90 ± 4.36
0.34
c
MMSE
26.68 ± 1.79
28.66 ± 1.43
28.74 ± 0.93
0.0001
Disease duration (years) H & Y stage
6.6 ± 3.7 1.5 ± 0.6
5.9 ± 2.6 1.5 ± 0.4
NA NA
0.61 0.91
14.3 ± 8.5
13.1 ± 5.3
NA
0.98 0.36
UPDRS III Daily levodopa dose (mg daily)
a
453.12 ± 307.39
530.88 ± 299.42
NA
DA agonists dose equivalent to levodopa dosageb
294.74 ± 124.60
285.52 ± 114.05
NA
0.78
WHM load (ml)
0.42 ± 0.43 (0–1.1)
0.40 ± 0.46 (0–0.78)
0.38 ± 0.43 (0–1.2)
0.87
MMSE MiniMental State Examination, H&Y stage Hoehn & Yahr stage, UPDRS Unified Parkinson’s Disease Rating Scale, WHM white matter hyperintensities a b c
16/21 PD-MCI patients and 17/21 PD-nMCI patients were treated with levodopa 9/21 PD-MCI patients and 19/21 PD-nMCI patients were treated with dopamine agonists at commonly used dosage Fisher’s exact test
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J Neurol (2015) 262:425–434
429
Table 2 Data (mean ± standard deviation) from cognitive testing in PD patients with (PD-MCI) and without mild cognitive impairment (PD-nMCI) and between-groups comparisons Cognitive domain
PD-MCI
Episodic memory domain Rey 15 words, 35.07 ± 6.28 immediate recall Rey 15 words, delayed recall
7.27 ± 2.78
PD-nMCI
p
41.31 ± 7.95
0.01
8.52 ± 2.33
NS
and motor variables (Table 1). As expected, the two groups differed significantly on several tests loading on memory, attention/executive, and visuospatial domains (Table 2). Among PD-MCI patients, 11 were impaired in one cognitive domain (all visuospatial), 9 patients showed dysfunction in 2 cognitive domains (7 visuospatial/attention-executive and 2 visuospatial/memory), and 1 patient resulted impaired in 3 cognitive domains (visuospatial/ attention-executive/memory).
Executive/attentional domain Frontal assessment battery
12.55 ± 2.03
15.41 ± 1.73
