dementia with Lewy bodies comparison

Neurobiology of Aging 34 (2013) 1148e1158

Contents lists available at SciVerse ScienceDirect

Neurobiology of Aging journal homepage: www.elsevier.com/locate/neuaging

Default network is not hypoactive in dementia with fluctuating cognition: an Alzheimer disease/dementia with Lewy bodies comparison Raffaella Franciotti a, b, c, Nicola Walter Falasca d, Laura Bonanni a, b, e, Francesca Anzellotti a, b, Valerio Maruotti a, b, Silvia Comani a, c, d, Astrid Thomas a, b, Armando Tartaro a, c, John-Paul Taylor f, Marco Onofrj a, b, * a

Department of Neuroscience and Imaging, “G. d’Annunzio” University, Chieti, Italy Aging Research Centre, Ce.S.I., “G. d’Annunzio” University Foundation, Chieti, Italy ITAB, “G. d’Annunzio” University Foundation, Chieti, Italy d BINDeBehavioral Imaging and Neural Dynamics Center, University of Chieti-Pescara, Chieti, Italy e Leonardo Foundation, Abano Terme, Italy f Institute for Ageing and Health, Newcastle University, Wolfson Research Centre, Campus for Ageing and Vitality, Newcastle upon Tyne, UK b c

a r t i c l e i n f o

a b s t r a c t

Article history: Received 15 June 2012 Received in revised form 4 September 2012 Accepted 12 September 2012 Available online 11 October 2012

Default mode network resting state activity in posterior cingulate cortex is abnormally reduced in Alzheimer disease (AD) patients. Fluctuating cognition and electroencephalogram abnormalities are established core and supportive elements respectively for the diagnosis of dementia with Lewy bodies (DLB). Our aim was to assess whether patients with DLB with both of these features have different default mode network patterns during resting state functional magnetic resonance imaging compared with AD. Eighteen patients with DLB, 18 AD patients without fluctuating cognition, and 15 control subjects were selected after appropriate matching and followed for 2e5 years to confirm diagnosis. Independent component analysis with functional connectivity (FC) and Granger causality approaches were applied to isolate and characterize resting state networks. FC was reduced in AD and DLB patients compared with control subjects. Posterior cingulate cortex activity was lower in AD than in control subjects and DLB patients (p < 0.05). Right hemisphere FC was reduced in DLB patients in comparison with control subjects but not in patients with AD, and was correlated with severity of fluctuations (r ¼ 0.69; p < 0.01). Causal flow analysis showed differences between patients with DLB and AD and control subjects. Ó 2013 Published by Elsevier Ltd.

Keywords: Alzheimer Disease Dementia with Lewy bodies Resting state fMRI Posterior cingulate cortex Independent component analysis Granger causality

1. Introduction Resting state functional magnetic resonance imaging (fMRI) shows synchronous low frequency activity of brain regions including ventral anterior and posterior cingulate cortex (PCC)/ precuneus, medial prefrontal cortex, and bilateral lateral and inferior parietal cortex (Broyd et al., 2009; Buckner et al., 2005). This network is defined “default mode network” (DMN) because it is typically active during a resting condition and deactivated when processing of external stimuli, is required (Binder et al., 1999). Several resting state studies showed DMN dysfunctions, consisting of hypoactivation and altered connectivity of the PCC/precuneus in Alzheimer’s disease (AD) (Greicius et al., 2004; Zhang et al., * Corresponding author at: Department of Neuroscience and Imaging, “G. d’Annunzio” University, Via dei Vestini 33, 66100 Chieti, Italy. Tel.: þ39 0871358525; fax: þ39 0871562019. E-mail address: [email protected] (M. Onofrj). 0197-4580/$ e see front matter Ó 2013 Published by Elsevier Ltd. http://dx.doi.org/10.1016/j.neurobiolaging.2012.09.015

2010). This, with the predilection for amyloid deposition in areas associated with the DMN (Buckner et al., 2009; Sheline et al., 2010) has given rise to the hypothesis that cortical hubs such as the DMN might be particularly affected in AD because of the fact that these hubs are subject to a high level of baseline activity and/or metabolism that makes them selectively vulnerable to AD pathology. AD is the most common type of dementia with a 40%e60% prevalence (Ferri et al., 2005), but other types of dementia represent a diagnostic challenge. Another common dementia is dementia with Lewy bodies (DLB) with a prevalence of 25%e40% (Kosaka and Iseki, 2000). Core symptoms of DLB include fluctuating cognition (fl Cog), visual hallucinations, and parkinsonism (McKeith et al., 2005). In particular, fl Cog consists of abnormal cognition episodes ranging from transient black-outs to delirious state and stupor, and it can occur with different frequencies, varying from sporadic to several times a day. Because of its functional impact, fl Cog represents the 1 of the most challenging core aspects of DLB (McKeith et al., 2005) yet its etiology is poorly understood.

R. Franciotti et al. / Neurobiology of Aging 34 (2013) 1148e1158

In DLB, the spatial topography of amyloid deposition is evident in areas similar to AD (Gomperts et al., 2008) and is inclusive of DMN nodes and thus it would not be unreasonable to expect that DLB patients should display abnormalities of the DMN. However, because DMN is supposedly an ontogenically developed system regulating fluctuations of consciousness (Binder et al., 1999), we further hypothesized that in DLB patients characterized by fl Cog, the DMN system would be relatively more abnormal than in AD patients without fl Cog. Only 2 recent studies investigated resting state and DMN activities also in DLB patients, with both finding reductions and increments of PCC connectivity involving different cortical areas (Galvin et al., 2011; Kenny et al., 2012). Patients were, however, not selected on the basis of fl Cog, although 1 of the studies indicated that understanding of this core feature might be elaborated on by further fMRI investigation (Kenny et al., 2012). Aside from etiologic uncertainties, clinical assessment of fl Cog is difficult because it is currently based on subjective interview rating systems, which were either specifically developed (Walker et al., 2000b) or derived from scales for the assessment of delirium (Escandon et al., 2010; Lee et al., 2012). Objectively, to date, the only reliable method which correlates with fl Cog is that of quantitative electroencephalogram (EEG), which is listed as a supportive element for DLB diagnosis (Barber et al., 2000; Bonanni et al., 2008; Briel et al., 1999; McKeith et al., 2005; Tateno et al., 2009; Walker et al., 2000c). Specific quantities in EEG activities correlate with fl Cog rating scales (Bonanni et al., 2008; Tateno et al., 2009; Walker et al., 2000c). EEG activity in DLB is characterized by variability of cortical rhythms on parietal-occipital derivations, consisting of increased representation of theta (or delta) rhythms, and in AD the rhythm is in the alpha range on the same derivations. Quantification of slow rhythms (slower than alpha) in these derivations allows for the expression of cutoffs which are able to separate AD from DLB patients (Bonanni et al., 2008; Franciotti et al., 2006; Walker et al., 2000c). Moreover, the most significant differences

1149

between AD and DLB were found in EEG epochs of 2- or 4-second length (Bonanni et al., 2008; Franciotti et al. 2006; Tateno et al., 2009; Walker et al., 2000a,c). Thus, the variability in the frequency of cortical EEG rhythms appear in time intervals sufficient to encompass the slower than EEG, spontaneous fluctuations of fMRI activity (Fox and Raichle, 2007). Thus considering the fl Cog/EEG correlation and time frame compatibility of EEG changes and fMRI assessment we hypothesized that any difference in resting state networks between AD and DLB would be maximized by consideration of subjects characterized with both a clinical measure (interview-based) and objective measure (EEG) of fl Cog. Therefore, subjects were carefully selected to ensure distinction between dementia groups. Only patients with an initial diagnosis which did not change over a 2e5 year follow-up were selected. All DLB patients included in the present study presented with fl Cog and visual hallucinations of variable severity, with resting state EEG abnormalities and with low dopamine transporter uptake in singlephoton emission computed tomography (SPECT) scan, and AD subjects had no evidence of fl Cog either clinically or on EEG. In fMRI data independent component analysis (ICA) was applied instead of the seed method, to account for variability of DMN topography across subjects (Anderson et al., 2011) and because of the hypothesis that fl Cog in DLB could lead to perturbed coactivation pattern of the DMN. 2. Methods 2.1. Study population The study population was recruited from our case cohorts included in previous studies (Bonanni et al., 2007, 2008; Franciotti et al., 2006; Lanuti et al., 2012; Onofrj et al., 2010). The study design is shown in Fig. 1. AD and DLB patients were matched for age,

Fig. 1. Flow chart of the study design. Abbreviations: AD, Alzheimer’s disease; CAF, Clinician Assessment of Fluctuation; CAM, Confusion Assessment Method; DLB, dementia with Lewy bodies; EEG, electroencephalogram; fMRI, functional magnetic resonance imaging; I-SPECT, ioflupane single-photon emission computed tomography; MRI, magnetic resonance imaging; NINCDS-ADRDA, National Institute of Neurological and Communicative Diseases and Stroke/Alzheimer’s Disease and Related Disorders Association; pts, patients; RBD, REM sleep behavior disorder; VH, visual hallucinations.

1150

R. Franciotti et al. / Neurobiology of Aging 34 (2013) 1148e1158

educational level, severity of dementia as assessed by Mini Mental State Examination (MMSE) and Clinical Dementia Rating (Morris, 1993). As a prerequisite, all DLB and none of the AD patients had fl Cog. Patients were also matched for gray matter volumes as assessed by voxel-based morphometry on 1.5 T magnetic resonance imaging (MRI) anatomic images. Fifteen percent of DLB and 35% of AD patients were excluded from study because of the presence of atrophy, vascular lesion, or white matter abnormalities (Supplementary Fig. 1 shows preselection). Fifteen healthy elderly controls, 18 AD, and 18 DLB patients at the onset of the disease participated in the study. All patients were not taking cholinesterase inhibitors when fMRI data were collected because these agents might reduce fl Cog (Burn et al., 2006; Onofrj et al., 2003). Treatments, according to patient needs, were only introduced during follow-up. Control subjects matched for age, sex, and education, were recruited from our nondemented case register cohort. Control subjects had no evidence of clinical dementia, all had MMSE scores >28, and no evidence of any significant abnormalities (e.g., cerebrovascular disease) on structural neuroimaging. Before being enrolled in the study, all subjects signed a written informed consent. The study was approved by our local ethical committee and was carried out according to the declaration of Helsinki and subsequent revisions (1997). For the diagnosis of dementia all patients met the statement of the American Psychiatric Association (1994). The diagnosis of probable AD was made according to National Institute of Neurological and Communicative Diseases and Stroke/Alzheimer’s Disease and Related Disorders Association criteria (McKhann et al., 1984). The clinical diagnosis of probable DLB was made according to criteria of the DLB Consortium (McKeith et al., 1996), including core and supportive elements. Patients were followed for 2e5 years to confirm diagnoses. Mean duration of follow-up was 4.3  1.0 (22 patients had 5-year follow-up, 8 patients 4-year, 2 patients 3-year and 4 patients 2year). Global test of cognition included also Dementia Rating Scale-2 (Jurica et al., 2001) and the Frontal Assessment Battery scale (Dubois et al., 2000). Visual hallucinations (VH) and other psychotic symptoms were assessed by the Neuropsychiatric Inventory (NPI) (Cummings et al., 1994). Presence and severity of cognitive fluctuations were evaluated with the Clinician Assessment of Fluctuation (CAF) (Walker et al., 2000a), the One-Day Fluctuation Assessment (Walker et al., 2000b), and the Confusion Assessment Method (Inouye et al., 1990). As in previous studies (Bonanni et al., 2007; Franciotti et al., 2006; Walker et al., 2000a,b), a CAF level of 5 or more, is predictive of DLB compared with AD (Walker et al., 2000b), and corresponds with severe fluctuations, and CAF scores below the cutoff were considered to be indicative of initial, less severe, fl Cog. Thus, the DLB group was dichotomized around CAF scores with the first group of 10 DLB patients having a CAF score 4 (low fluctuators), and the second group consisted of 8 DLB patients with CAF score ranging between 5 and 12 (high fluctuators). REM Sleep Behavior Disorder, a supportive element for DLB diagnosis recently shown to be the most predictive element for neuropathologic confirmation (Boeve, 2010), was evaluated according to minimal International Classification of Sleep Disorders (World Health Organization, 1992) and confirmed by polysomnographic recordings as in previous studies on the same cohort (Bonanni et al., 2008; Onofrj et al., 2010). Parkinsonian motor signs were rated with the motor part of the Unified Parkinson’s Disease Rating Scale (Fahn and Elton, 1987) and Hoehn/Yahr scale (Hoehn and Yahr, 1967). Dopaminergic presynaptic ligand ioflupane SPECT (I-SPECT) was performed in all patients 2e4 months after initial diagnosis.

All subjects underwent EEG recording at admission to the study and on the day of fMRI recordings. Because EEG parameters provide further confirmation of fluctuations (Bonanni et al., 2008; Walker et al., 2000a) and the DLB diagnosis (Tateno et al., 2009), EEG quantification (method detailed in Supplementary data on Quantitative EEG) was focused on variability measures according to previous studies (Bonanni et al., 2008; Walker et al., 2000a,c). Because of the correlation of slow wave EEG activity with fl Cog, AD patients with prominent slow-wave EEG activities were excluded from the study (Supplementary Fig. 1). Only DLB patients with abnormal EEG patterns consisting of increased variability of the dominant frequency, shifting from alpha to theta and/or reduced presence of alpha band, substituted by dominant theta bands (6.5e7.5 Hz) in frontal, temporal, and parieto-occipital derivations (p < 0.01) were included in the study. See Supplementary data on Quantitative EEG for EEG quantifications and cutoffs. 2.2. FMRI acquisition Functional images were acquired with a Philips scanner at 1.5 T by means of T2*-weighted echo planar imaging with the following parameters: echo time ¼ 50 ms; field of view ¼ 240 mm; in-plane voxel size ¼ 3.75  3.75 mm2; slice thickness ¼ 8 mm; no gap. Functional volumes consisted of 16 bicommissural slices acquired with a volume repetition time of 1409 ms. A total of 5 trials were acquired during resting state with eyes closed; 200 volumes were acquired for each trial. Volumetric images were acquired via a 3-D T1-TFE (Turbo Field Echo) sequence. 1.5 T MRI was used instead of 3 T MRI because patient selection and first acquisitions began in 2005. FMRI data were collected in each patient 1.5e4 months after the initial diagnosis. 2.3. Data analysis FMRI data analyses were carried out using Brain Voyager Qx release 2.3 (Brain Innovation, The Netherlands). The first 5 functional volumes of each trial were discarded to account for T1 saturation effects. Data preprocessing included slice timing correction and slice realignment for head motion correction. For each subject fMRI data were coregistered with their own 3-D anatomic images that were transformed into stereotaxic coordinates of the Talairach space. The results of the automatic realignment process were verified visually. The Talairach transformation matrix was applied to functional images. Spatial smoothing was achieved with an 8-mm Gaussian core full-width half-maximum. Single-subject spatial ICA was applied to functional data to identify low-frequency neural networks during resting state. For each of the 5 trials, 30 independent components (ICs) were extracted on singlesubject data using the “FastICA” algorithm. Thirty ICs were the maximum model order obtained on all patients and subjects for all runs. Furthermore, the use of 30 ICs led to stable estimations. Cluster size was fixed to 10 mm for each dimension and z threshold of 2.5 was used as a criterion to establish which brain regions contributed significantly to each component map. We decided to apply ICA on the entire dataset to enhance signal to noise ratio and to allow for a better selection of the regions of interest (ROIs) included in the DMN. Each IC consists of a temporal waveform and an associated z score spatial map that reflects the degree at which a given voxel time course correlates with the corresponding IC waveform. Self-organizing clustering approach was applied to each subject to combine 5 trials into a single dataset based upon a similarity measure between components. The validity of this approach has been demonstrated for a blind extraction and selection of meaningful activity and functional connectivity patterns

R. Franciotti et al. / Neurobiology of Aging 34 (2013) 1148e1158

(Esposito et al., 2005). For each subject, the final spatial maps were checked visually to select the components that accounted for potential decomposition of the DMN into more than 1 spatial map (Damoiseaux et al., 2006). Coactivation of the posterior cingulate cortex and the bilateral inferior parietal lobule (Koch et al., 2012) was used at the criterion to select ICs that most closely matched the DMN pattern. In order to exclude from the selected DMN components that might be artifacts, peak frequency and the ICA fingerprint (a polar representation of the component in a multidimensional space of spatial, temporal, and spectral parameters that includes: degree of clustering, skewness, kurtosis, spatial entropy, 1 lag autocorrelation, temporal entropy, power) were evaluated (De Martino et al., 2007). In each subject 9 ROIs were identified based on the ICs that passed all selection criteria. Each ROI contained about 1000 voxels and ROIs were centered on the DMN clusters using a center of gravity approach (Koch et al., 2012). The mean blood oxygen level-dependent (BOLD) signal intensities across all voxels in each ROI were extracted from each trial and converted to z-score values. The extracted signals were not processed to exclude other contributions because the causal flow analysis requires raw signals. Self-organizing clustering was then applied to each group (control, AD, and DLB) to combine components from each subject and to obtain group spatial maps. Considering our a priori hypotheses that specific DMN nodes would be affected and to enhance sensitivity in detecting group differences, subject groups were analyzed separately in terms of ROI and functional connectivity (FC); therefore the reported comparisons pertain to the DMN patterns and node regions rather than a voxel-level comparative analysis. Spectral distribution of the BOLD signals from each ROI was performed to represent the power of low frequency fluctuations (LFF) for each ROI (Yang et al., 2007). Then, the power of LFF, a possible biomarker of spontaneous activity, was evaluated as the integral of the spectral power in the predominant frequency band ranging from 0.01 to 0.1 Hz. In order to perform FC analysis, for each ROI the z-score values obtained from the time course of the BOLD signal in each trial were joined sequentially in the 5 trials. We thus obtained a vector representing the time courses of the z-score signals sequentially in the 5 trials. The dimension of this concatenated vector was 5 times the dimension of the z-score vector for a single trial. Pearson product moment correlation coefficients (r) of pairwise ROIs were then calculated on the time courses of z-score signals for each subject. Square correlation matrices were generated for each subject and subsequently averaged across all subjects in each group to obtain a mean functional correlation matrix. Granger causality analysis (GCA) computed by a Matlab toolbox (Seth, 2010) was performed to estimate the direction of information flow across areas using statistical relationships among simultaneously observed BOLD time series. Causal flow analysis is independent from mutual trend between areas, thus GCA results are not biased by the possible differences in the trend of the signals in definite temporal intervals. A detailed description of GCA, and the rationale for the choice of GCA is reported in the Supplementary data on Granger causality analysis.

1151

Table 1 Demographic, clinical, and neuropsychological data at admission to the study and at follow-up Group Control, n ¼ 15

AD, n ¼ 18

Age (y)a 74  2 76  1 Male sex 33% 39% a 71 61 Educational level (y) I-SPECT, abnormal uptake 0% 0% Clinical dementia rating (CDR)a Admission 00 1.1  0.2 Follow-up 00 1.4  0.3 a Mini Mental State Examination (MMSE) Admission 28.9  0.8 20.4  0.6 Follow-up 28.5  0.7 16.4  0.6 Neuropsychiatry Inventory (NPI)a Admission 4.1  0.4 15.7  2.2 Follow-up 4.6  0.4 22.1  1.7 Dementia Rating Scale-2 (DRS-2)a Admission 137.2  0.7 101.8  1.31 Follow-up 136.1  0.6 91.4  1.5 a Frontal Assessment Battery (FAB) Admission 17.8  0.4 15.1  0.4 Follow-up 17.5  0.9 13.7  0.6 Clinician Assessment of Fluctuation (CAF)a Admission 00 00 Follow-up 00 00 a One-Day Fluctuation Assessment (ODFA) Admission 00 00 Follow-up 00 1.4  0.2 Confusion Assessment Method (CAM), positive score (all items) Admission 0% 0% Follow-up 0% 11% REM Sleep Behavior Disorder (RBD) Admission 0% 0% Follow-up 0% 0% Visual hallucinations (VH) Admission 0% 0% Follow-up 0% 0% Hallucinations item 2-NPIa Admission 00 00 Follow-up 00 00

DLB, n ¼ 18 75  1 50% 71 100% 1.1  0.2 1.5  0.3 20.6  0.5 17.6  0.5 25.8  1.9 31.4  1.7 97.6  1.6 88.3  1.5 13.4  0.5 10.5  0.6 3.7  0.3 5.6  0.4 5.9  0.4 8.6  0.5 44% 100% 61% 100% 100% 100% 5.7  0.5 6.2  0.4

All DLB patients (100%) had low dopamine transporter uptake evidenced by I-SPECT scan and positive CAM scores at follow-up. For the different items scores, admission indicates values obtained 2e4 wk before fMRI recording. Follow-up indicates the same scores obtained after 2-y follow-up. VH are reported in % of patients presenting with VH according to NPI specific item. Key: AD, Alzheimer’s disease; DLB, dementia with Lewy bodies; fMRI, functional magnetic resonance imaging; I-SPECT, ioflupane single-photon emission computed tomography. a Values are reported as mean  standard error.

Pearson correlation analysis was performed between FC values and test scores to evaluate the relationship between connectivity strengths and clinical/neuropsychological scores. In the between-group analysis on FC, general linear model multivariate analysis of variance was used with r values as dependent variables and group as factor. Bonferroni post hoc test was used to correct for multiple comparisons. The level of significance in GCA was set to p < 0.05 for each subject.

3. Results 2.4. Statistics

3.1. Demographic and clinical characterization

Analysis of variance was performed for statistical comparison across groups on demographic and clinical characteristics as well as on EEG variables and on LFF power from frontal parietal areas and PCC. In each group the activity levels of each node were ordered according to their mean power, using Kendall’s W test.

Table 1 shows demographic and clinical characteristics in the 3 groups (control, AD, and DLB) at admission to the study and at follow-up. All DLB patients had low dopamine transporter uptake in basal ganglia on I-SPECT imaging (bilateral in 16 of 18 patients). At admission, CAF scores were between 2 and 12 in DLB patients and

1152

R. Franciotti et al. / Neurobiology of Aging 34 (2013) 1148e1158

Clinical Dementia Rating, and for Dementia Rating Scale-2. Presence of REM Sleep Behavior Disorder, VH, and positive scores on CAF and One-Day Fluctuation Assessment at admission significantly separated DLB from AD patients (p < 0.01). In addition, NPI total scores were significantly higher in DLB than in AD patients (p < 0.02). At the end of the follow-up period all DLB and none of the AD patients presented with VH. No correlation was found between NPI-Hallucinations subscale item and CAF scores (r ¼ 0.2; p > 0.1). CAF scores correlated with EEG abnormalities (r ¼ 0.6; p < 0.01), which was consistent with findings from previous reports (Bonanni et al., 2008; Walker et al., 2000a). Fig. 2 shows an example of EEG variability evidenced by the compressed spectral array method in a control subject, in an AD patient, and in 2 DLB patients. EEG variables are reported in Supplementary Table 2.

3.2. Functional connectivity

Fig. 2. Compressed spectral arrays of a control, a patient with Alzheimer’s disease (AD), and 2 patients with dementia with Lewy bodies (DLB). Compressed spectral arrays of 20 epochs of 2 seconds each, recorded from parietal derivations. The salient frequency peaks indicate the frequency expressing the main power for each epoch. In the control (CTL) and AD patient, the dominant peak (DF) is stable in the alpha frequency, and in DLB patients alpha appears in less than 50% of epochs (with the dominant frequency shifting from alpha to theta or not appearing [last row] at all) and is substituted by theta. Numbers above traces point to dominant frequency in Hz. Abbreviation: fl Cog, fluctuating cognition.

always equal to 0 in AD and control subjects; Confusion Assessment Method scores were positive in 44% of DLB patients and negative in 100% of AD and control subjects. One-Day Fluctuation Assessment scores ranged from 6 to 12 in the DLB patients and were 0 in AD and control subjects. At admission but also at the 2-year follow-up, no differences between AD and DLB patients were found for MMSE,

In each participant ICA confirmed the presence of typical DMN patterns, defined as coactivation of the PCC, the left and right lateral parietal cortex (RLPC, LLPC), left and right inferior parietal lobule (LIPL, RIPL), and left and right superior frontal gyrus (LSFS, RSFS) and left and right middle frontal gyrus (LMFG, RMFG) (Fig. 3). PCC, LIPL, RIPL, LSFS, and RSFS were positively correlated with IC waveform (red spots in Fig. 3), whereas LMFG, RMFG, LLPC, and RLPC were negatively correlated with IC waveform (blue spot in Fig. 3). Supplementary Table 4 shows the mean Talairach coordinates of DMN areas obtained from each group ICA. The LFF activity levels varied across the regions. The Kendall’s coefficient of Concordance (KcC), and related order of decreasing value, was: in the control group, KcC ¼ 0.141 (p ¼ 0.03), RMFG < LMFG < LSFS < RSFS < RIPL < LIPL < LLPC < RLPC < PCC; in the AD group, KcC ¼ 0.151 (p ¼ 0.005), LMFG < RSFS < LSFS < RMFG < RIPL < LIPL < LLPC < PCC < RLPC; in the DLB group, KcC ¼ 0.282 (p < 0.001), RSFS < RMFG < LMFG < LSFS < LLPC < RLPC < LIPL < RIPL < PCC.

Fig. 3. (A) Default mode network (DMN) spatial maps obtained from independent component analysis (ICA) on control, Alzheimer’s disease (AD), and dementia with Lewy bodies (DLB) groups. Yellow-red and green-blue areas indicate positive and negative correlation with the independent component (IC) waveform respectively. (B) Polar representations of spatial, temporal and spectral parameters (fingerprints) used to exclude ICs derived from artifacts.

R. Franciotti et al. / Neurobiology of Aging 34 (2013) 1148e1158

Fig. 4. Group averages (15 control, 18 Alzheimer’s disease [AD], 18 dementia with Lewy bodies [DLB]) of low frequency fluctuation (LFF) power. Representations are given in percentages for the frontal areas, parietal areas, and posterior cingulate cortex (PCC). Vertical bars indicate standard errors.

Thus, activity levels were lower in frontal than in parietal areas in both hemispheres in all subject groups. The PCC area showed the highest activity level in control subjects and DLB patients (Fig. 4). Analysis of variance showed significant main effects for groups (F(2,48) ¼ 3.2; p < 0.05) and areas (F(2,96) ¼ 28; p < 0.0001): the DMN activity was highest in the PCC and lower in frontal than in parietal areas (p < 0.01). Post hoc tests revealed that the overall DMN activity was lower in AD than control and DLB groups (p < 0.05). When we selected the PCC mean activity value of the AD group as cutoff, the PCC activity reduction showed a sensitivity of 0.56, and separated AD from control and DLB groups with a specificity of 0.73 and 0.89 respectively. FC was significantly different across groups for the following areas: LSFS and RSFS (p ¼ 0.04), LSFS and RIPL (p < 0.04), RSFS and LIPL (p ¼ 0.02), RSFS and RIPL (p < 0.001), RSFS and PCC (p ¼ 0.04), LMFG and RMFG (p < 0.04), LLPC and RLPC (p ¼ 0.02), and RIPL and PCC (p < 0.05). Loss of interhemispheric connectivity in frontal and parietal areas was found in DLB patients compared with control subjects. In the right hemisphere there was reduced connectivity between frontal and parietal areas in DLB and AD patients compared with control subjects. Table 2 shows Bonferroni post hoc tests and FC values for each group. Fig. 5 shows area pairs characterized by significant differences in FC values between groups. 3.3. FC correlations In AD patients significant Pearson correlations (p < 0.05) were found between MMSE scores and FC values in LSFS and RIPL (r ¼ 0.47), RSFS and LIPL (r ¼ 0.54), and in RSFS and RIPL (r ¼ 0.56). In DLB patients, a significant correlation was found between CAF scores and FC values in RMFG and RLPC (r ¼ 0.69; p < 0.01) but no significant correlations between MMSE scores and FC values (r < 0.2; p > 0.1). Fig. 6 shows scatter plots for significant correlations. The statistical comparison between control subjects, AD

1153

patients, and the 10 DLB low fluctuators showed a significant difference in the FC between RMFG and RLPC (p < 0.05). Specifically, FC was more reduced in AD patients than in DLB low fluctuators (p < 0.03). FC values in RMFG and RLPC were lower in DLB high fluctuators than in DLB low fluctuators (p < 0.01) showing that in DLB patients with CAF scores 4, FC between middle frontal gyrus and lateral parietal cortex (LPC) in the right hemisphere was unaffected compared with control subjects. No difference was found in the comparison of FC of RMFG and RLPC between AD and DLB patients with CAF score 5 (high fluctuators). Thus, the impairment of the connection between right frontal and parietal lobe in DLB appears to be associated with cognitive fluctuations. 3.4. Granger causality A causal connectivity network graph was superimposed on structural MRI for each group. Links characterized by significant Granger causality magnitudes and with difference of influence greater than 0 (see Supplementary data on Granger causality analysis) were identified for each group. Directions of causal statistical influences are shown in Fig. 7. The directions of information flow from PCC to LIPL, RIPL and RSFS were present in all groups, whereas the causal influences among the other nodes were different across groups (Fig. 7). For the control and DLB groups, the PCC was the strongest hub and for the AD group the connections involving PCC were reduced (Fig. 7). In the control and AD groups, interhemispheric causal flow was present in the LPC from the left to the right hemisphere and intrahemispheric connections from inferior parietal lobule to superior frontal gyrus were also evident in the left and in the right hemisphere. However, there was no interhemispheric causal interaction in the DLB group. In addition, compared with the AD and control groups, DLB patients showed a causal influence from the LIPL to the LSFS and in the right hemisphere there was a causality direction of influence going from the middle frontal gyrus to the LPC. Subgroup analysis of the DLB group on the basis of the severity of their fl Cog on the CAF scores demonstrated though the direction of influence from RMFG to RLPC remained evident in DLB low fluctuators (CAF  4), DLB patients with CAF 5 (n ¼ 8), which corresponds with severe fluctuations, there was a loss of information on directionality in the frontoparietal connections in the right hemisphere (Fig. 7). These results thus confirmed evidence obtained with FC analysis, which showed a correlation between frontoparietal connection in the right hemisphere and fl Cog in DLB patients. 4. Discussion By restricting the selected populations to DLB patients with fl Cog and to AD patients without, our study aimed to highlight

Table 2 Functional Connectivity (FC) values among brain couples showing significant differences between groups Brain connections

Control

AD

DLB

Control versus AD

FC values LSFS-RSFS LSFS-RIPL RSFS-LIPL RSFS-RIPL RSFS-PCC LMFG-RMFG LLPC-RLPC RIPL-PCC

0.72 0.56 0.58 0.69 0.62 0.66 0.71 0.69

       

0.03 0.04 0.03 0.03 0.04 0.03 0.03 0.03

Control versus DLB

Post hoc 0.57 0.41 0.42 0.51 0.44 0.53 0.59 0.54

       

0.05 0.05 0.04 0.04 0.05 0.05 0.05 0.06

0.63 0.46 0.43 0.54 0.47 0.50 0.54 0.63

       

0.03 0.04 0.05 0.03 0.06 0.04 0.04 0.40

p< p¼ p< p< p< d d p<

0.04 0.03 0.04 0.01 0.05

0.05

d d d p ¼ 0.02 d p ¼ 0.04 p ¼ 0.02 d

Brain connections are coupled between the different cortical areas. FC values are reported as mean  standard error. p values from Bonferroni post hoc tests. Key: AD, Alzheimer’s disease; DLB, dementia with Lewy bodies; FC, functional connectivity; LIPL, left inferior parietal lobe; LLPC, left lateral parietal cortex; LMFG, left middle frontal gyrus; LSFS, left superior frontal sulcus; PCC, posterior cingulate cortex; RIPL, right inferior parietal lobe; RLPC, right lateral parietal cortex; RMFG, right middle frontal gyrus; RSFS, right superior frontal sulcus.

1154

R. Franciotti et al. / Neurobiology of Aging 34 (2013) 1148e1158

Fig. 5. Comparison of functional connectivity (FC) values across groups. The yellow lines indicate cerebral connections showing significant FC differences (p < 0.05) between control and Alzheimer’s disease (AD) group and between control and dementia with Lewy bodies (DLB) group. No differences were observed in direct comparison between AD and DLB. Abbreviations: LIPL, left inferior parietal lobule; LLPC, left lateral parietal cortex; LMFG, left middle frontal gyrus; LSFS, left superior frontal gyrus; PCC, posterior cingulate cortex; RIPL, right inferior parietal lobule; RLPC, right lateral parietal cortex; RMFG, right middle frontal gyrus; RSFS, right superior frontal gyrus.

possible abnormalities of DMN in DLB during resting state. The prediction was that in DLB patients with fl Cog the disruption of resting state DMN network would be evident because these patients had clear resting state EEG abnormalities, which is in contrast to AD patients who have a predominantly normal dominance of alpha rhythm at rest. Unexpectedly, however, DMN coactivation could be identified also in DLB patients with severe cognitive fluctuations, and clear EEG abnormalities although

several differences on FC strength from control subjects were found that correlated significantly with CAF scores. By using LFF, ICA, and FC analysis as well as Granger Causality (analyzing directionality of mutual influence among areas), DMN differences from control subjects pointed to a predominant PCC activity alteration in AD and predominant right hemisphere involvement in DLB (Figs. 4 and 5). Direct comparison between AD and DLB groups showed significant reduction of PCC activity in AD. In AD, the overall activity of the DMN was lower than in control subjects and DLB patients, and the reduction was mainly related to a decrease of PCC activity, and in DLB patients the engagement of the PCC was not altered (Fig. 4). In DLB reductions in connectivity between interhemispheric left-right parietal connections and frontoparietal areas in the right hemisphere (Figs. 5 and 7) were found only in comparison with control subjects. FC alterations were correlated with MMSE scores in AD, and in DLB the only correlation found was between the severity of fluctuation (CAF scores) and decreased connectivity of right frontoparietal areas (Fig. 6). Causality analysis (GCA) confirmed FC findings, by showing reduced information flow in PCC of AD and the lack of the interhemispheric information flow between parietal areas in DLB (Fig. 7), statistically separating DLB from AD patients and control subjects. We considered these findings as less sound than the PCC preserved activity, because the right hemisphere FC was reduced in AD as much as in DLB patients with severe fl Cog, and was not different from control subjects in DLB patients with less severe fl Cog. In DLB patients with severe fl Cog, GCA revealed disappearance of frontoparietal causal information flow in the right hemisphere (Fig. 7). Therefore our data-driven ICA study confirms previous findings showing a specific abnormality of PCC connections in AD (Galvin et al., 2011; Zhang et al., 2010). The unexpected finding obtained in our study is that the presence of fl Cog accompanied by rest EEG abnormalities does not

Fig. 6. Scatterplot of the functional connectivity (FC) and test scores. (A) Correlation between right superior frontal gyrus (RSFS) and left inferior parietal lobule (LIPL) functional connectivity (FC) and Mini Mental State Examination (MMSE) scores in Alzheimer’s disease (AD) patients. (B) Correlation between RSFS and right inferior parietal lobule (RIPL) FC and MMSE scores in AD patients. (C) Correlation between left superior frontal gyrus (LSFS) and RIPL FC and MMSE scores in AD patients. (D) Correlation between right middle frontal gyrus (RMFG) and right lateral parietal cortex (RLPC) FC and Clinician Assessment of Fluctuations (CAF) scores in dementia with Lewy bodies (DLB) patients.

R. Franciotti et al. / Neurobiology of Aging 34 (2013) 1148e1158

1155

Fig. 7. Granger causality results for the 3 groups: control, Alzheimer’s disease (AD), and dementia with Lewy bodies (DLB) (A) and for the DLB subgroups differentiated on the basis of their Clinician Assessment of Fluctuation (CAF) scores (CAF  4 and CAF  5) (B). The arrow indicates the direction of significant causal influence assessed by the difference of influence. The strength of the causal connections (Granger causality magnitude) was expressed by the thickness of the yellow line. Abbreviations: LIPL, left inferior parietal lobule; LLPC, left lateral parietal cortex; LMFG, left middle frontal gyrus; LSFS, left superior frontal gyrus; PCC, posterior cingulate cortex; RIPL, right inferior parietal lobule; RLPC, right lateral parietal cortex; RMFG, right middle frontal gyrus; RSFS, right superior frontal gyrus.

imply a severe alteration of DMN. Differences between DLB patients with fl Cog and AD patients without fl Cog consist of different patterns of connectivity, but there is no evidence of a DMN system disruption in presence of fl Cog. PCC, the main DMN hub, is instead actively recruiting causal correlations with other nodes. Secondary analysis performed by separating DLB patients with moderate and severe fl Cog, showed that the PCC hub could be clearly identified even in severely fluctuating patients (Fig. 7). The 2 previous studies on resting state fMRI of DLB patients showed different areas of abnormal activity. Galvin et al. (2011) found increased connectivity of precuneus with putamen and parietal regions and decreased connectivity with prefrontal and primary visual cortices whereas Kenny et al., (2012) found increased connectivity of the PCC and normal connectivity with visual cortex. Methodologic differences can however explain the different findings. Both studies used a seed approach, using precuneus and PCC which is prone to placement bias. In our study instead we used ICA in order to (1) avoid previous spatial assumptions and noise associated with seeded areas; (2) provide the ability to compare the coherent activity in multiple distributed voxels (Cole et al., 2010); and (3) reduce the number of identified areas and also show robust activity of DMN areas. In both previous studies the core element fl Cog was not tested, low dopamine uptake evidenced by SPECT or positron emission tomography perfusion was not used to select all patients. Our study was focused on fl Cog, using EEG as a support for the identification

of this core element, because EEG was the only instrumental biomarker shown to correlate with fl Cog (Walker et al., 2000b). Based on these adjunctive selection criteria, the main conclusion from our study is that DMN, in DLB patients characterized by fl Cog, is as active as in control subjects, in agreement with the 2 previous studies showing normal to increased PCC activity in DLB (Galvin et al., 2011; Kenny et al., 2012). A possible interpretation of these findings, as reported by Kenny et al. (2012) could be that maintenance or an increase PCC activity in DLB is the result of compensatory mechanisms attempting to maintain homeostatically DMN functions in the face of developing pathology. Following this interpretation the lack of compensatory mechanism undermining this homeostasis in AD might be because of the greater cellular loss which occurs in this disease compared with DLB (Szpak et al., 2001). However an alternative interpretation might be proposed: we suggest that in AD, frontal lobe inhibitory afferent projections to DMN hub PCC compensates for reduced temporal lobe input to the DMN (Firbank et al., 2007). In DLB, given the known and greater involvement of frontal areas compared with AD (Geser et al., 2005; Sanchez-Castaneda et al., 2010), frontal activity might be insufficient to inhibit DMN. Our study showed that DMN main hub, PCC function, is preserved in DLB patients across the full range of fl Cog severity. Previous studies have demonstrated that DMN activity is increased during attention lapses in control subjects

1156

R. Franciotti et al. / Neurobiology of Aging 34 (2013) 1148e1158

(Weissman et al., 2006), and in head trauma patients affected by attention deficits (Bonnelle et al., 2011), and it has been hypothesized that hallucinations in Parkinson disease might be dependent on fluctuating cognition linked to insufficient inhibition of DMN (Shine et al., 2011). Thus it could be argued that preserved DMN activity is the cause, rather than the effect of fluctuating cognition, and that DMN dysfunction might have a role in determining different phenotypes in dementia (e.g., a phenotype characterized by fluctuations in arousal and alertness with oniroid imagery and hallucinations which is linked to persistent DMN activity; DLB-like), and a phenotype linked to deficient DMN activity might be characterized by unsuppressed arousal and consequent anxiety (ADlike). Based on this hypothesis we could predict that the activity of DMN might change during the course of different neurodegenerative disorders, inducing a prominent anxiety-dependent phenotype, such as Godot Syndrome of AD (Reisberg et al., 1996), or a confusion-dependent phenotype, similar to DLB with fl Cog and visual hallucinations or indeed the more profound delirium-like states that occur in the later stages of AD. Additional studies in AD patients with advanced disease, when fl Cog often occurs (Escandon et al., 2010; Ferman et al., 2004), could help in understanding whether the hypoactivation of PCC persists in AD, or changes, or different mechanisms underlie the occurrence of fl Cog in AD. In our study we also found dysfunction of right hemisphere FC in DLB patients with fl Cog, correlating with CAF scores. However in AD there was also evidence of a reduction of right hemisphere FC (Fig. 5), despite the fact that all the AD patients included in the study did not have fl Cog. A study in advanced AD patients, where fl Cog is more common, might show whether the occurrence of confusion correlates with PCC or right hemisphere FC, and thus whether fl Cog in different diseases are dependent on different mechanisms. In addition, we are cautious to overinterpret the FC differences within our DLB group between high and low fl Cog (as separated on the CAF scores) because of the small group sizes in this subanalysis and therefore these findings need confirmation in a larger sample. Finally the concluding remark of our study must be addressed to its strengths and limitations. Strengths of this study were that the DLB and AD groups were subject to full clinical and cognitive assessment and rigorous diagnosis, including I-SPECT and matching for sex, age, cognitive examination scores, and including the best available assessments of fl Cog, and patients were tested before cholinesterase inhibitor treatment which could potentially have confounded the fl Cog and resting state BOLD observations. A specific limitation was linked to the second-most relevant finding of our study: we found a loss of interhemispheric connectivity in frontal and parietal areas and impaired right hemisphere connections in DLB patients with fl Cog. Our results might suggest a specific role of right hemisphere dysfunction in the occurrence of fl Cog (Fig. 7, Table 2). This finding could be interpreted on the basis of known anatomic correlates of attention, as evidenced by studies on left neglect or on exogenous and endogenous attention pathways (Fox et al., 2006; Mesulam, 1985). However, our study was not designed to investigate attention or salience networks which could only be evaluated in a more complex study design, which includes assessments of DMN inhibition during a task protocol. The limit of our study is therefore that a definitive clarification of the neural mechanisms underlying fluctuations should come from further studies assessing both task and rest conditions, evidencing whether DMN in patients with fl Cog is not inhibited during task execution. A previous study (Sauer et al., 2006) investigated visual task-related changes in DLB and, in agreement with our conclusion, found reduced DMN inhibition during a series of visual tasks. However, the duration of such task/rest protocols could be prohibitively

demanding for dementia patients and performance-mediated confounder effects might reduce the validity of any changes in observed task-related BOLD activity, thus forcing researchers to draw conclusions from data obtained only with the less demanding, resting state protocol, like in our and in most of other studies (Broyd et al., 2009; Galvin et al., 2011; Greicius et al., 2004; Kenny et al., 2012; Koch et al., 2012; Zhang et al., 2010). Nevertheless, these limitations will need to be overcome as only by the integration of different study paradigms will one be able to clarify definitively the mechanisms underlying fl Cog. Use of passive and undemanding tasks which can be managed by dementia patients (Taylor et al., 2012) might be helpful in this regard. In conclusion, our primary aim was to determine if there were selective differences in DMN between DLB (particularly those with fl Cog) and AD patients and thus we focused on this network rather than performing a more wide-reaching voxel-based group comparison; this obviously enhanced our power to address our primary hypothesis but limited the scope of our study in potentially detecting specific (sub)regions of differences between the groups which have been more salient in contributing to fl Cog; future studies considering this and the role of other resting state networks might be helpful. Disclosure statement M. Onofrj has served as a consultant for UCB, Novartis, Lundbeck Medtronic Newron Boheringher Ingelheim; serves on speakers’ bureaus for the Movement Disorders Society, World Parkinson Association, and on the editorial board of European Neurological Journal. All authors declare no conflicts of interest. Before being enrolled in the study, all subjects signed a written informed consent. The study was approved by our local ethical committee and was carried out according to the declaration of Helsinki and subsequent revisions. Acknowledgements The authors thank Professor M. Corbetta and G.B. Frisoni for careful reading of the manuscript. This study was supported by the Italian National Institute of Health (Grant Young Researcher 2007). Appendix A. Supplementary data Supplementary data associated with this article can be found, in the online version, at http://dx.doi.org/10.1016/j.neurobiolaging. 2012.09.015. References American Psychiatric Association, 1994. Diagnostic and Statistical Manual of Mental Disorders, fourth ed. American Psychiatric Press, Washington, DC. Anderson, J.S., Ferguson, M.A., Lopez-Larson, M., Yurgelun-Todd, D., 2011. Connectivity gradients between the default mode and attention control networks. Brain Connect. 1, 147e157. Barber, P.A., Varma, A.R., Lloyd, J.J., Haworth, B., Snowden, J.S., Neary, D., 2000. The electroencephalogram in dementia with Lewy bodies. Acta Neurol. Scand. 101, 53e56. Binder, J.R., Frost, J.A., Hammeke, T.A., Bellgowan, P.S., Rao, S.M., Cox, R.W., 1999. Conceptual processing during the conscious resting state. A functional MRI study. J. Cogn. Neurosci. 11, 80e95. Boeve, B.F., 2010. REM sleep behavior disorder: updated review of the core features. The REM sleep behavior disorder-neurodegenerative disease association, evolving concepts, controversies, and future directions. Ann. N. Y. Acad. Sci. 1184, 15e54. Bonanni, L., Anzellotti, F., Varanese, S., Thomas, A., Manzoli, L., Onofrj, M., 2007. Delayed blink reflex in dementia with Lewy bodies. J. Neurol. Neurosurg. Psychiatry 78, 1137e1139. Bonanni, L., Thomas, A., Tiraboschi, P., Perfetti, B., Varanese, S., Onofrj, M., 2008. EEG comparisons in early Alzheimer’s disease, dementia with Lewy bodies and

R. Franciotti et al. / Neurobiology of Aging 34 (2013) 1148e1158 Parkinson’s disease with dementia patients with a 2-year follow-up. Brain 131, 690e705. Bonnelle, V., Leech, R., Kinnunen, K.M., Ham, T.E., Beckmann, C.F., De Boissezon, X., Greenwood, R.J., Sharp, D.J., 2011. Default mode network connectivity predicts sustained attention deficits after traumatic brain injury. J. Neurosci. 31, 13442e13451. Briel, R.C., McKeith, I.G., Barker, W.A., Hewitt, Y., Perry, R.H., Ince, P.G., Fairbairn, A.F., 1999. EEG findings in dementia with Lewy bodies and Alzheimer’s disease. J. Neurol. Neurosurg. Psychiatry 66, 401e403. Broyd, S.J., Demanuele, C., Debener, S., Helps, S.K., James, C.J., Sonuga-Barke, E.J., 2009. Default-mode brain dysfunction in mental disorders: a systematic review. Neurosci. Biobehav. Rev. 33, 279e296. Buckner, R.L., Sepulcre, J., Talukdar, T., Krienen, F.M., Liu, H., Hedden, T., AndrewsHanna, J.R., Sperling, R.A., Johnson, K.A., 2009. Cortical hubs revealed by intrinsic functional connectivity: mapping, assessment of stability, and relation to Alzheimer’s disease. J. Neurosci. 29, 1860e1873. Buckner, R.L., Snyder, A.Z., Shannon, B.J., LaRossa, G., Sachs, R., Fotenos, A.F., Sheline, Y.I., Klunk, W.E., Mathis, C.A., Morris, J.C., Mintun, M.A., 2005. Molecular, structural, and functional characterization of Alzheimer’s disease: evidence for a relationship between default activity, amyloid, and memory. J. Neurosci. 25, 7709e7717. Burn, D., Emre, M., McKeith, I., De Deyn, P.P., Aarsland, D., Hsu, C., Lane, R., 2006. Effects of rivastigmine in patients with and without visual hallucinations in dementia associated with Parkinson’s disease. Mov. Disord. 21, 1899e1907. Cole, D.M., Smith, S.M., Beckmann, C.F., 2010. Advances and pitfalls in the analysis and interpretation of resting-state FMRI data. Front. Syst. Neurosci. 4, 8. Cummings, J.L., Mega, M., Gray, K., Rosenberg-Thompson, S., Carusi, D.A., Gornbein, J., 1994. The neuropsychiatric inventory: comprehensive assessment of psychopathology in dementia. Neurology 44, 2308e2314. Damoiseaux, J.S., Rombouts, S.A., Barkhof, F., Scheltens, P., Stam, C.J., Smith, S.M., Beckmann, C.F., 2006. Consistent resting-state networks across healthy subjects. Proc. Natl. Acad. Sci. U. S. A. 103, 13848e13853. De Martino, F., Gentile, F., Esposito, F., Balsi, M., Di Salle, F., Goebel, R., Formisano, E., 2007. Classification of fMRI independent components using IC-fingerprints and support vector machine classifiers. Neuroimage 34, 177e194. Dubois, B., Slachevsky, A., Litvan, I., Pillon, B., 2000. The FAB: a Frontal Assessment Battery at bedside. Neurology 55, 1621e1626. Escandon, A., Al-Hammadi, N., Galvin, J.E., 2010. Effect of cognitive fluctuation on neuropsychological performance in aging and dementia. Neurology 74, 210e217. Esposito, F., Scarabino, T., Hyvarinen, A., Himberg, J., Formisano, E., Comani, S., Tedeschi, G., Goebel, R., Seifritz, E., Di Salle, F., 2005. Independent component analysis of fMRI group studies by self-organizing clustering. Neuroimage 25, 193e205. Fahn, S., Elton, R.L., 1987. Members of the Unified Parkinson’s Disease Rating Scale Development Committee, Unified Parkinson’s disease rating scale. Vol. 2. In: Fahn, S., Marsden, C.D., Calne, D.B., Goldstein, M. (Eds.), Recent Development in Parkinson’s Disease. Macmillan Healthcare Information, Florham Park, NJ, pp. 153e164. Ferman, T.J., Smith, G.E., Boeve, B.F., Ivnik, R.J., Petersen, R.C., Knopman, D., GraffRadford, N., Parisi, J., Dickson, D.W., 2004. DLB fluctuations. Specific features that reliably differentiate DLB from AD and normal aging. Neurology 62, 181e187. Ferri, C.P., Prince, M., Brayne, C., Brodaty, H., Fratiglioni, L., Ganguli, M., Hall, K., Hasegawa, K., Hendrie, H., Huang, Y., Jorm, A., Mathers, C., Menezes, P.R., Rimmer, E., Scazufca, M., Alzheimer’s Disease International, 2005. Global prevalence of dementia: a Delphi consensus study. Lancet 366, 2112e2117. Firbank, M.J., Blamire, A.M., Krishnan, M.S., Teodorczuk, A., English, P., Gholkar, A., Harrison, R.M., O’Brien, J.T., 2007. Diffusion tensor imaging in dementia with Lewy bodies and Alzheimer’s disease. Psychiatry Res. 155, 135e145. Fox, M.D., Corbetta, M., Snyder, A.Z., Vincent, J.L., Raichle, M.E., 2006. Spontaneous neuronal activity distinguishes human dorsal and ventral attention systems. Proc. Natl. Acad. Sci. U. S. A. 103, 10046e10051. Fox, M.D., Raichle, M.E., 2007. Spontaneous fluctuations in brain activity observed with functional magnetic resonance imaging. Nat. Rev. Neurosci. 8, 700e711. Franciotti, R., Iacono, D., Della Penna, S., Pizzella, V., Torquati, K., Onofrj, M., Romani, G.L., 2006. Cortical rhythms reactivity in AD, LBD and normal subjects: a quantitative MEG study. Neurobiol. Aging 27, 1100e1109. Galvin, J.E., Price, J.L., Yan, Z., Morris, J.C., Sheline, Y.I., 2011. Resting bold fMRI differentiates dementia with Lewy bodies vs Alzheimer disease. Neurology 76, 1797e1803. Geser, F., Wenning, G.K., Poewe, W., McKeith, I., 2005. How to diagnose dementia with Lewy bodies: state of the art. Mov. Disord. 20, S11eS20. Gomperts, S.N., Rentz, D.M., Moran, E., Becker, J.A., Locascio, J.J., Klunk, W.E., Mathis, C.A., Elmaleh, D.R., Shoup, T., Fischman, A.J., Hyman, B.T., Growdon, J.H., Johnson, K.A., 2008. Imaging amyloid deposition in Lewy body diseases. Neurology 71, 903e910. Greicius, M.D., Srivastava, G., Reiss, A.L., Menon, V., 2004. Default-mode network activity distinguishes Alzheimer’s disease from healthy aging: evidence from functional MRI. Proc. Natl. Acad. Sci. U. S. A. 101, 4637e4642. Hoehn, M.M., Yahr, M.D., 1967. Parkinsonism: onset, progression and mortality. Neurology 17, 427e442. Inouye, S.K., van Dyck, C.H., Alessi, C.A., Balkin, S., Siegal, A.P., Horwitz, R.I., 1990. Clarifying confusion: the confusion assessment method. A new method for detection of delirium. Ann. Intern. Med. 113, 941e948.

1157

Jurica, P.J., Leitten, C.L., Mattis, S., 2001. DRS-2 Dementia rating scale 2. Psychological Assessment Resources, Odessa, FL. Kenny, E.R., Blamire, A.M., Firbank, M.J., O’Brien, J.T., 2012. Functional connectivity in cortical regions in dementia with Lewy bodies and Alzheimer’s disease. Brain 135, 569e581. Koch, W., Teipel, S., Mueller, S., Benninghoff, J., Wagner, M., Bokde, A.L., Hampel, H., Coates, U., Reiser, M., Meindl, T., 2012. Diagnostic power of default mode network resting state fMRI in the detection of Alzheimer’s disease. Neurobiol. Aging 33, 466e478. Kosaka, K., Iseki, E., 2000. Clinicopathological studies on diffuse Lewy body disease. Neuropathology 20, 1e7. Lanuti, P., Ciccocioppo, F., Bonanni, L., Marchisio, M., Lachmann, R., Tabet, N., Pierdomenico, L., Santavenere, E., Catinella, V., Iacone, A., Thomas, A., Gambi, D., Miscia, S., Onofrj, M., Kern, F., 2012. Amyloid-specific T-cells differentiate Alzheimer’s disease from Lewy body dementia. Neurobiol. Aging 33, 2599e2611. Lee, D.R., Taylor, J.P., Thomas, A.J., 2012. Assessment of cognitive fluctuation in dementia: a systematic review of the literature. Int. J. Geriatr. Psychiatry 27, 989e998. McKeith, I.G., Dickson, D.W., Lowe, J., Emre, M., O’Brien, J.T., Feldman, H., Cummings, J., Duda, J.E., Lippa, C., Perry, E.K., Aarsland, D., Arai, H., Ballard, C.G., Boeve, B., Burn, D.J., Costa, D., Del Ser, T., Dubois, B., Galasko, D., Gauthier, S., Goetz, C.G., Gomez-Tortosa, E., Halliday, G., Hansen, L.A., Hardy, J., Iwatsubo, T., Kalaria, R.N., Kaufer, D., Kenny, R.A., Korczyn, A., Kosaka, K., Lee, V.M., Lees, A., Litvan, I., Londos, E., Lopez, O.L., Minoshima, S., Mizuno, Y., Molina, J.A., Mukaetova-Ladinska, E.B., Pasquier, F., Perry, R.H., Schulz, J.B., Trojanowski, J.Q., Yamada, M., Consortium on DLB, 2005. Diagnosis and management of dementia with Lewy bodies: third report of the DLB Consortium. Neurology 65, 1863e1872. McKeith, I.G., Galasko, D., Kosaka, K., Perry, E.K., Dickson, D.W., Hansen, L.A., 1996. Consensus guidelines for the clinical and pathologic diagnosis of dementia with Lewy bodies (DLB): report of the consortium on DLB international workshop. Neurology 47, 1113e1124. McKhann, G., Drachman, D., Folstein, M., Katzman, R., Price, D., Stadlan, E.M., 1984. Clinical diagnosis of Alzheimer’s disease: report of the NINCDSADRDA work group under the auspices of Department of Health and Human Services Task Force on Alzheimer’s Disease. Neurology 34, 939e944. Mesulam, M.M., 1985. Attention, confusional states, and neglect. In: Mesulam M.M. (Ed), Principles of Behavioural Neurology. Contemporary Neurology series. F.A. Davis, Philadelphia, pp. 174e238. Morris, J.C., 1993. The Clinical Dementia Rating (CDR): current version and scoring rules. Neurology 43, 2412e2414. Onofrj, M., Bonanni, L., Manzoli, L., Thomas, A., 2010. Cohort study on somatoform disorders in Parkinson disease and dementia with Lewy bodies. Neurology 74, 1598e1606. Onofrj, M., Thomas, A., Iacono, D., Luciano, A.L., Di Iorio, A., 2003. The effects of a cholinesterase inhibitor are prominent in patients with fluctuating cognition: a part 3 study of the main mechanism of cholinesterase inhibitors in dementia. Clin. Neuropharmacol. 26, 239e251. Reisberg, B., Auer, S.R., Monteiro, I.M., 1996. Behavioral pathology in Alzheimer’s disease (BEHAVE-AD) rating scale. Review. Int. Psychogeriatr. 8, 301e308. Sanchez-Castaneda, C., Rene, R., Ramirez-Ruiz, B., Campdelacreu, J., Gascon, J., Falcon, C., Calopa, M., Jauma, S., Juncadella, M., Junque, C., 2010. Frontal and associative visual areas related to visual hallucinations in dementia with Lewy bodies and Parkinson’s disease with dementia. Mov. Disord. 25, 615e622. Sauer, J., ffytche, D.H., Ballard, C., Brown, R.G., Howard, R., 2006. Differences between Alzheimer’s disease and dementia with Lewy bodies: an fMRI study of task-related brain activity. Brain 129, 1780e1788. Seth, A.K., 2010. A MATLAB toolbox for Granger causal connectivity analysis. J. Neurosci. Methods 186, 262e273. Sheline, Y.I., Raichle, M.E., Snyder, A.Z., Morris, J.C., Head, D., Wang, S., Mintun, M.A., 2010. Amyloid plaques disrupt resting state default mode network connectivity in cognitively normal elderly. Biol. Psychiatry 67, 584e587. Shine, J.M., Halliday, G.M., Naismith, S.L., Lewis, S.J., 2011. Visual misperceptions and hallucinations in Parkinson’s disease: dysfunction of attentional control networks? Mov. Disord. 26, 2154e2159. Szpak, G.M., Lechowicz, W., Lewandowska, E., Bertrand, E., Wierzba-Bobrowicz, T., Gwiazda, E., Schmidt-Sidor, B., Dymecki, J., 2001. Neurones and microglia in central nervous system immune response to degenerative processes. Part 1: Alzheimer’s disease and Lewy body variant of Alzheimer’s disease. Quantitative study. Folia Neuropathol. 39, 181e192. Tateno, M., Kobayashi, S., Saito, T., 2009. Imaging improves diagnosis of dementia with lewy bodies. Psychiatry Investig. 6, 233e240. Taylor, J.P., Firbank, M.J., He, J., Barnett, N., Pearce, S., Livingstone, A., Vuong, Q., McKeith, I.G., O’Brien, J.T., 2012. Visual cortex in dementia with Lewy bodies: magnetic resonance imaging study. Br. J. Psychiatry 200, 491e498. Walker, M.P., Ayre, G.A., Cummings, J.L., Wesnes, K., McKeith, I.G., O’Brien, J.T., Ballard, C.G., 2000a. Quantifying fluctuation in dementia with Lewy bodies, Alzheimer’s disease, and vascular dementia. Neurology 54, 1616e1625. Walker, M.P., Ayre, G.A., Cummings, J.L., Wesnes, K., McKeith, I.G., O’Brien, J.T., Ballard, C., 2000b. The clinician assessment of fluctuation and the one day fluctuation assessment scale. Two methods to assess fluctuating confusion in dementia. Br. J. Psychiatry 177, 252e256.

1158

R. Franciotti et al. / Neurobiology of Aging 34 (2013) 1148e1158

Walker, M.P., Ayre, G.A., Perry, E.K., Wesnes, K., McKeith, I.G., Tovee, M., Edwardson, J.A., Ballard, C.G., 2000c. Quantification and characterization of fluctuating cognition in dementia with Lewy bodies and Alzheimer’s disease. Dement. Geriatr. Cogn. Disord. 11, 327e335. Weissman, D.H., Roberts, K.C., Visscher, K.M., Woldorff, M.G., 2006. The neural bases of momentary lapses in attention. Nat. Neurosci. 9, 971e978. World Health Organization, 1992. The ICD-10 Classification of Mental and Behavioural Disorders. World Health Organization, Geneva.

World Medical Association Declaration of Helsinki: recommendation guiding physicians in biomedical research involving human subjects. JAMA 277,1997, 925e926. Yang, H., Long, X.Y., Yang, Y., Yan, H., Zhu, C.Z., Zhou, X.P., Zang, Y.F., Gong, Q.Y., 2007. Amplitude of low frequency fluctuation within visual areas revealed by restingstate functional MRI. Neuroimage 36, 144e152. Zhang, H.Y., Wang, S.J., Liu, B., Ma, Z.L., Yang, M., Zhang, Z.J., Teng, G.J., 2010. Resting brain connectivity: changes during the progress of Alzheimer disease. Radiology 256, 598e606.