Psychiatry Research: Neuroimaging 221 (2014) 37–42
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Whole cortical and default mode network mean functional connectivity as potential biomarkers for mild Alzheimer's disease Marcio Luiz Figueredo Balthazar a,n, Brunno Machado de Campos a, Alexandre Rosa Franco b, Benito Pereira Damasceno a, Fernando Cendes a a b
Department of Neurology, University of Campinas (Unicamp), Sao Paulo 13083-970, Brazil Brain Institute of Rio Grande do Sul, Catholic University of Rio Grande do Sul (PUCRS), Porto Alegre, Brazil
art ic l e i nf o
a b s t r a c t
Article history: Received 24 April 2013 Received in revised form 6 August 2013 Accepted 31 October 2013 Available online 11 November 2013
The search for an Alzheimer's disease (AD) biomarker is one of the most relevant contemporary research topics due to the high prevalence and social costs of the disease. Functional connectivity (FC) of the default mode network (DMN) is a plausible candidate for such a biomarker. We evaluated 22 patients with mild AD and 26 age- and gender-matched healthy controls. All subjects underwent resting functional magnetic resonance imaging (fMRI) in a 3.0 T scanner. To identify the DMN, seed-based FC of the posterior cingulate was calculated. We also measured the sensitivity/specificity of the method, and verified a correlation with cognitive performance. We found a significant difference between patients with mild AD and controls in average z-scores: DMN, whole cortical positive (WCP) and absolute values. DMN individual values showed a sensitivity of 77.3% and specificity of 70%. DMN and WCP values were correlated to global cognition and episodic memory performance. We showed that individual measures of DMN connectivity could be considered a promising method to differentiate AD, even at an early phase, from normal aging. Further studies with larger numbers of participants, as well as validation of normal values, are needed for more definitive conclusions. & 2013 Elsevier Ireland Ltd. All rights reserved.
Keywords: Default mode network Biomarker Resting state fMRI Dementia
1. Introduction The search for an Alzheimer's disease (AD) biomarker is one of the most relevant research topics of the decade, largely due to the prevalence and social cost of the disease. A number of studies using biological fluids (such as cerebrospinal and plasma) as well as structural, functional, and molecular neuroimaging, are being conducted to improve the accuracy of AD diagnosis. Functional neuroimaging methods, as exemplified by functional magnetic resonance imaging (fMRI), have promise as biomarkers for AD, especially those obtained under resting conditions or without a specific task (“resting-state” fMRI, fc-fMRI or intrinsic brain activity). These methods have been helpful in identifying different functional connectivity networks (FC) through interregional correlation analysis of spontaneous fluctuations in the blood oxygenation level-dependent (BOLD) signal. Such studies have shown that synchronous oscillations in the BOLD signal, usually at a low frequency (0.01 to 0.1 Hz), represent functional neural networks
n Correspondence to: Department of Neurology, FCM, University of Campinas (UNICAMP), Cidade Universitaria, Campinas-SP, Brazil, 13083-970. Tel.: þ 55 19 3521 9217. E-mail addresses: [email protected], [email protected] (M.L.F. Balthazar).
0925-4927/$ - see front matter & 2013 Elsevier Ireland Ltd. All rights reserved. http://dx.doi.org/10.1016/j.pscychresns.2013.10.010
that are spatially distinct but functionally connected (Van den Heuvel and Hulshoff Pol, 2010). The main theoretical assumption for investigating FC disruption as a potential biomarker is that neurodegenerative diseases like AD are among those that can damage the neurofunctional organization of mental activity, leading to both cognitive and psychiatric symptoms. Seeley et al. (2009) demonstrated the network degeneration hypothesis across five distinct dementia syndromes, including AD. In typical AD, the progression of symptoms occurs in a stereotyped order, relating to the topographic progression of pathology: episodic memory loss takes place first (hippocampus and medial temporal lobe, along with posterior cingulate cortex), followed by problems with language and semantic memory (lateral temporal cortex) and visuospatial skills (temporal, and parietal neocortex). This orderly progression might be indicative of degeneration throughout interconnected regions within large-scale networks, which will ultimately spread into adjacent regions (Pievani et al., 2011). The default mode network (DMN) is the most studied network in AD. The function of the DMN is not completely understood, but it is accepted that there is increased activity when the patient is not focused on activities directed at the external environment (e.g., when an individual recalls autobiographical facts and events, or plans for the future). Some AD studies have shown a breakdown
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in functional connectivity in the DMN even at early stages of the disease (Greicius et al., 2004; Zhang et al., 2010; Jones et al., 2011; Prvulovic et al., 2011), but there are no well-established methods to detect this alteration on an individual basis. In this study, we investigated the relevance of the DMN and whole cortical mean functional connectivity as potential biomarkers in mild AD, through a seed-based approach in the posterior cingulate cortex—which is a well-established DMN anatomical region and known as one of the central hubs of the network. We looked at whether whole cortical mean connectivity in relation to posterior cingulate cortex, as well as DMN mean connectivity, might differentiate AD patients from age-matched healthy comparison subjects at the group and individual level. We also verified if the DMN and whole cortical connectivity were related to cognition. 2. Methods 2.1. Subjects Forty-eight subjects over age 50 were enrolled in this study, of whom 22 showed mild AD (16 women), and were followed at the Neuropsychology and Dementia Outpatient Clinic (UNICAMP University Hospital). For comparison, there were 26 healthy age- and gender-matched control subjects (20 women). Routine laboratory studies were performed on all patients, including B12 levels, folate levels, syphilis serology, and thyroid hormone levels. The study was approved by the Medical Research Ethics Committee of Medical Sciences School—UNICAMP. Local ethical committee approval and written informed consent (either from the subjects or from their responsible guardians if incapable) were obtained before study initiation, according to the Declaration of Helsinki. The diagnosis of probable AD was based on criteria established by the National Institute of Neurological and Communicative Disorders and the Stroke and the Alzheimer's Disease and Related Disorders Association (NINCDS/ADRDA) (McKhann et al., 1984). We only included patients who were classified with a clinical dementia rating (CDR)¼ 1. The exclusion criteria included a history of other neurological or psychiatric diseases, previous head injury with loss of consciousness, drug or alcohol addiction, prior chronic exposure to neurotoxic substances, and a Hachinski ischaemic score44. The control group consisted of subjects who were classified as CDR¼ 0 and had no history of neurologic disease, psychiatric disease, or memory complaints. 2.2. Neuropsychological, neuropsychiatric, and functional evaluations Global cognitive status was assessed with the Mini-Mental State Examination (MMSE) (Folstein et al., 1975; Brucki et al., 2003). We evaluated episodic memory with the Rey Auditory Verbal Learning Test (RAVLT) (Rey, 1964), and visual perception with subtests of Luria's Neuropsychological Investigation (LNI) (Christensen, 1975). Mental rotation was assessed using four items from the Ratcliff Manikin Test (Ratcliff, 1979). Evaluation of language included the Boston Naming Test (BNT) (Kaplan et al., 1983), verbal fluency for category (animals) and phonological awareness with the Foreign Accent Syndrome (FAS). We assessed constructive praxis with the Rey–Osterrieth complex figure test (ROCF) (Osterrieth, 1944), executive function with the trail making test (TMT) A and B, the stroop colorword test (SCWT), and the clock drawing test (CDT), and working memory with the forward (FDS) and backward digit span (BDS) subtest of the Wechsler adult intelligence scale (WAIS-R) (Wechsler, 1987). We applied the CDR with a semistructured interview (Morris, 1993). Data analysis was performed using SPSS software (version 20). We performed a t-test for intergroup comparisons of demographic and cognitive scores. When the data violated the assumptions of parametric tests, we performed the Mann– Whitney test. The results were considered to be statistically significant when p o0.05. 2.3. MRI acquisition Structural and functional images were acquired on a 3 T MRI scanner (Philips Achieva, Best, Netherlands). A set of structural images was acquired with the following sequences: (a) sagittal high-resolution T1-weighted with gradient echo images, repetition time/echo time (TR/TE)¼ 7/3.2 ms, field of view (FOV) ¼ 240 240, and isotropic voxels of 1 mm3; (b) coronal and axial FLAIR (Fluidattenuated Inversion Recovery) T2-weighted images, anatomically aligned at the hippocampus with image parameters set to TR/TE/TI (inversion time) ¼ 12000/140/ 2850, FOV ¼ 220 206, voxels reconstructed to 0.45 0.45 4.00 mm3 and gap between slices set to 1 mm; (c) coronal IR (inversion recovery) T1-weighted images with TR/TE/TI¼ 3550/15/400, FOV ¼180 180 and voxels reconstructed to
0.42 0.42 3.00 mm3; and (d) coronal multi-echo (5 echoes) T2-weighted images with TR/TE ¼3300/30, FOV ¼ 180 180, voxels reconstructed to 0.42 0.42 3.00 mm3. All images were reviewed to exclude other associated pathologies. Functional images were acquired under resting conditions. Subjects were instructed to keep their eyes closed and not to think of anything in particular. Axial T2n-weighted images had TR/TE ¼2000/30 ms, FOV ¼240 240, and isotropic voxels set to 3 3 3 mm3. For each participant, we acquired 10-min Echo Planar Images (EPI) and discarded the first three volumes to avoid T1 effects. The total of all remaining images corresponds to 300 volumes with 40 axial slices each. The participants’ sensory stimulation was limited to noise of the scanner during image acquisition. The subjects used earplugs to reduce this noise. In addition, all subjects had their head movements restricted by a soft Velcro strap. 2.4. Functional imaging analyses Functional images were initially preprocessed by applying slice-time and motion-correction algorithms. Linear trends were also removed from these data. Data pre-processing also included smoothing with a 6-mm FWHM (full width at half-maximum) Gaussian kernel and bandpass filtering (0.008 to 0.1 Hz). For spatial normalization, structural images where first linearly registered to the Montreal Neurological Institute (MNI)152 (standard space) with a 12-parameter affine transformation. The resulting image was again registered to the MNI152 image, now using a non-linear warping algorithm. Functional data were first registered to the structural image using a six-parameter affine transformation, then warped to standard space using the transformations calculated for the structural image. Six parameters of head motion, as well as cerebrospinal fluid and white matter time series, were regressed as nuisance variables. To identify the DMN, seed-based functional connectivity was calculated by placing a seed in the posterior cingulate cortex (0, 51, 15; MNI; seed radius¼ 3 mm). Specifically, for each subject, the average time course of voxels within this seed was extracted and generated a reference time series. Each time series was then correlated with all the voxels within the brain for each subject. Subsequently, r-scores of each voxel were then transformed using Fisher's r-to-z method, so that these data could be used in parametric statistical analysis to obtain whole cortical statistical z-score maps. All steps were performed with AFNI (http://afni.nimh.nih.gov/afni) and FSL (http://fsl. fmrib.ox.ac.uk/fsl/fslwiki/) software. We calculated an average z-score for each subject, considering (1) whole cortical absolute z-average (WCA), (2) whole cortical positive z-average (WCP), and (3) DMN positive z-average. To calculate the DMN z-average, we used a mask of DMN based on our control subjects’ statistical maps (z-score). These images were created according to the study methodology, with a seed placed on the posterior cingulate cortex (PCC). All maps were used to create an average image that was smoothed (FWHM ¼6 6 6 mm3) and binarized using a minimum threshold of 0.3 (z-score value). We compared the results between groups by using an independent two-sample t-test. We also calculated the sensitivity and specificity of the method, including a ROC (receiver operating characteristic) curve analysis. In addition, we verified if there was a significant correlation between DMN and WCP z-scores with global cognition (as measured by the MMSE) and episodic memory (as measured by RAVLT encoding, delayed recall, and recognition) to evaluate whether connectivity measures were related to cognition. We also performed a simple linear regression, while looking at AD and normal aging groups together. The results were assessed as statistically significant when P o0.05.
3. Results Table 1 presents demographic and cognitive results. There was no difference between patients and controls for gender and age, although controls had higher educational levels. The MMSE average score of our patients was 18.86, but we still considered them as mild AD for two main reasons: (1) Our elderly population had few years of formal education, especially among our public hospital patients (mean: 5.95 years, Table 1). In this study, three subjects were illiterate. Brucki et al. (2003) suggested that the normal MMSE values for the Brazilian population could be 20 for illiterates and 25 for those with 1 to 4 years of education. (2) We included only patients with CDR 1, and the mean CDR sum of boxes was 4.97. O'Bryant et al. (2008) showed that the optimal range of CDR-sum of boxes scores for a global score of 1.0 (mild) was 4.5 to 9.0. We found a significant statistical difference between mild AD and controls considering DMN's positive z average (t¼3.504; df¼46; P ¼0.001), WCP (positive z average, t¼2.934, df ¼46; P¼ 0.005), and WCA z-score values (t ¼2.567; df ¼46; P ¼0.01) (Fig. 1).
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Table 1 Demographic, functional and neuropsychological data.
Age (years) Education (years) Mini-mental status examination Clinical dementia rating—sum of boxes RAVLT—Encoding RAVLT—Delayed Recall RAVLT—Recognition Boston naming test (0–60) Semantic verbal fluency Phonologic verbal fluency (FAS) Visuospatial skills—LNI Forward digit span Backward digit span Trail making test-B (seconds) Stroop test- congruent (seconds) Stroop test- congruent (errors) Stroop test- incongruent (seconds) Stroop test-incongruent (errors) Clock drawing test (0–10) Rey complex figure (copy)
AD
Controls
p
73.40 7 5.67 5.95 7 5.17 18.86 7 4.68 4.977 0.87 18.137 6.75 0.50 7 0.67 3.85 7 4.89 29.53 7 13.80 9.317 4.78 18.42 7 12.51 14.727 2.93 3.777 1.47 1.727 1.27 278.737 56.60 79.42 7 43.75 0.477 0.92 179.95 7 78.59 33.527 21.98 5.09 7 2.81 13.24.04 7 14.57
71.03 7 6.61 10.22 7 5.55 28.59 7 1.86 0 47.54 78.06 9.047 2.41 11.95 7 2.53 52.047 4.93 17.63 74.99 33.27 711.72 18.09 7 1.19 5.137 1.88 4.187 1.33 138.047 95.49 56.047 17.60 0.047 0.21 102.09 7 27.35 2.86 7 3.73 9.40 7 1.50 34.88 7 3.81
0.19n o 0.05 o 0.0001 o 0.0001 o 0.0001 o 0.0001 o 0.0001 o 0.0001n o 0.0001 o 0.0001 0.01n 0.001n o 0.0001 0.001 0.62 o 0.0001n o 0.0001 0.0001 o 0.0001
Neuropsychological tests were performed in all subjects. Data presented as mean 7 SD. RAVLT: Rey auditory verbal learning test; LNI: Luria's Neuropsychological Investigation. n
Mann Whitney test was applied due lack of normality.
Fig. 1. Box-and-whiskers plot showing mean connectivity z scores: (A) Default mode network positive z average (DMN): mild AD (0.23 7 0.06) and controls (0.29 7 0.05). (B) Whole cortical positive z average (WCP): mild AD (0177 0.04) and controls (0.217 0.03). The box extends from the 25th percentile to the 75th percentile, with a horizontal line at the median. Whiskers extend down to the smallest value and up to the largest.
For DMN individual values, we found a sensitivity of 77.3% and a specificity of 70%, when using a cutoff z-score of 0.267. Area under the ROC curve is 0.767 (Fig. 2A). For WCP individual values, we found a sensitivity of 72.7% and a specificity of 70%, when using a cutoff z-score of 0.195. Area under the ROC curve is 0.731, and the standard error is 0.67 (Fig. 2B). In addition, when considering the two groups together, we found a significant correlation with DMN z-scores and MMSE score (r ¼ 0.45, P ¼0.001) and with RAVLT encoding (r ¼0.34, P ¼0.01), delayed recall (A7; r ¼ 0.32, P ¼0.02), and recognition (r ¼0.43; P ¼0.002) (Fig. 3). WCP z-scores were significantly correlated with MMSE scores (r ¼0.40; P ¼0.004) and RAVLT recognition (r ¼0.36, P ¼0.006) (Fig. 4). None of the other possible correlations with other neuropsychological tests reached statistical significance. Furthermore, when we performed these correlations only in the AD group, no statistically significant results were obtained.
4. Discussion We have shown that individual measures of DMN, as well as whole cortical connectivity in relation to DMN in the posterior cingulate cortex, can be considered a promising method to differentiate AD from normal aging, even in its early phases. At a group level, we found a significant difference between patients and controls (P¼ 0.001 for DMN and P¼ 0.005 for the WCP). At an individual level, our findings were less substantial, but also noteworthy: we found a sensitivity of 77.3% and a specificity of 70% when we considered the mean DMN connectivity (area under curve is 76.7%). In addition, we found that these connectivity measures were related to global cognition and episodic memory performance. DMN positive z-scores were superior to WCP in differentiating groups and individuals, and were most significantly correlated with cognition.
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Fig. 2. ROC curves in DMN and WCP.
Fig. 3. Simple linear regressions between DMN z scores and cognition. (A) DMN MMSE; (B) DMN RAVLT's encoding; (C) DMN RAVLT's delayed recall; (D) DMN RAVLT's recognition.
The cerebral structure of cognitive functions is organized in several functional networks, which dynamically interact with each other. Neurodegenerative diseases like AD, however, disrupt this organization and cause cognitive and psychiatric symptoms. Alterations in DMN functional connectivity have been increasingly recognized in AD, as this FC represents a characteristic set of brain
regions, which are among the first to show abnormal amyloid deposition (a key pathological feature of AD). This includes areas like the precuneus, posterior cingulate cortex, medial prefrontal cortex, lateral temporal and parietal cortices, and hippocampus (Sheline and Raichle, 2013). Drzezga et al. (2011) reported a relationship of global functional connectivity with amyloid
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Fig. 4. Simple linear regressions between WCP z scores and cognition. (A) WCP MMSE; (B) WCP RAVLT's recognition.
depositions and hypometabolism in the precuneus and posterior cingulate cortex, even in the healthy elderly; they used positron emission tomography (PET) with Pittsburgh compound-B (PETPIB) (a sensitive marker of amyloid pathology in patients with AD). They hypothesized that the emerging amyloid load could initiate the synaptic dysfunction, which causes disruption in the functional connectivity in these regions. Adriaanse et al. (2012) also found reduced DMN connectivity in subjects with AD, but did not find an association of functional connectivity in the DMN with amyloid load. Some authors showed an association between lower DMN deactivation years before clinical symptoms of AD appeared (and a spontaneous neuronal activation higher than expected) and the amyloid load (Buckner et al., 2008). According to this theory (“metabolic hypothesis”), functional alterations in DMN could precede the pathological changes that typically occur in AD. The metabolic hypothesis is supported by recent studies, demonstrating that functional alterations precede atrophy in the posterior cingulate cortex (Gili et al., 2011), and that asymptomatic carriers of Apolipoprotein ε4 (ApoE-4) have a connectivity reduction in the posterior cingulate cortex (Machulda et al., 2011). Several authors found differences in DMN connectivity between AD and normal aging at a group level; however, there are fewer studies that examined differences on an individual level (Greicius et al., 2004; Wang et al., 2007; Zhang et al., 2010; Drzezga et al., 2011; Pievani et al., 2011; Adriaanse et al., 2012; Koch et al., 2012). Different fMRI methods have been used for this purpose. Greicius et al. (2004) evaluated individual measures of DMN integrity to separate AD from normal aging in an indirect way: they compared the fit of a subject's DMN to a standard default mode template. These authors also used another method to obtain the DMN (independent component analysis - ICA) in 13 patients and 13 controls, and found a sensitivity of 85% and a specificity of 77% to differentiate these subjects. In a recent study, Koch et al. (2012) combined two approaches: ICA- and seed-based interconnectivity analyses. While each approach revealed moderate results when used separately, a combination of both methods (within a multivariate approach) showed a sensitivity of 100% and specificity of 95% in discriminating AD patients from healthy subjects. Supekar et al. (2008) demonstrated a loss of smallworld properties in AD patients, characterized by a significantly lower clustering coefficient; this was most apparent in the left and right hippocampus, which indicated disrupted local connectivity. The clustering coefficient distinguished AD participants from the controls, with a sensitivity of 72% and specificity of 78%. DMN disruptions can also be related to cognitive problems in AD. Their exact role is not known, but there are some hypotheses
about its function. As discussed by Buckner et al. (2008), DMN can be associated with spontaneous cognition, in which human thought is separate from the immediate environment. Its activation is characterized by mental exploration based on personal introspection, autobiographical memories, and thoughts about the future. In the healthy brain, greater suppression of the DMN is associated with better memory formation (Daselaar et al., 2009). Other authors have reported a correlation between memory and DMN; Celone et al. (2006) found associations between DMN connectivity and memory performance. Westlye et al. (2011) showed a negative correlation between DMN synchronization and performance on memory tests in healthy elderly individual carriers of ApoE-4, a gene associated with an increased risk factor for sporadic AD. Binnewijzend et al. (2012) also found an association between cognition and functional connectivity measures in DMN, including the MMSE and episodic memory. Our results emphasized the relevance of DMN connectivity in cognition, specifically concerning episodic memory. These findings are in accordance with those of Seeley et al. (2009): their hypothesis suggested that different neurodegenerative syndromes affect different FCs, and thus cause different cognitive and behavioral profiles. In this view, AD could be considered as having a specific DMN problem in its initial phase, while also causing early episodic memory dysfunction. Resting state fMRI has many advantages in the study of AD: its noninvasive nature, the short time for imaging acquisition (6 to 10 min), and the fact that it can be done without any specific task: AD patients may be too impaired to actively participate in a taskbased scanning paradigm (Sheline and Raichle, 2013). In addition, analyses of this seed-based method are relatively simple and can be fully automated. Our study did not reach sensitivity/specificity greater than 80%, but the DMN findings represent a measure that could be a clinically relevant biomarker for AD. Functional connectivity as assessed by fMRI is a relatively new research field, and technical advances in the acquisition and analyses of data are increasingly being studied. For example, Boyacioglu and Barth (2012) described a new fMRI technique (ultrafast fMRIGeneralized iNverse imaging-GIN), which increases temporal resolution (50 ms) without losing spatial resolution. Increasing the magnetic field may also be useful in fMRI studies in the future. De Martino et al. (2011) demonstrated that high-resolution images acquired at 7 T might allow for separation of detailed spatial features within the DMN, including a better correlation with anatomy. Although we know that functional connectivity measures may be influenced by atrophy, as we discussed in a recent article
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(Balthazar et al., 2013), we believe that correcting fMRI results for atrophy is not necessarily useful in the context of biomarker research, when the main goal is just to differentiate patients and normal subjects. Our work should encourage further longitudinal studies examining mean DMN and WCP z-scores, connectivity measures in AD, and mild cognitive impairment. Increasing the sample of control subjects, selecting specific areas of DMN rather than the whole network, and evaluating the data in native space might improve the statistical strength of this method.
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Contents lists available at ScienceDirect
Psychiatry Research: Neuroimaging journal homepage: www.elsevier.com/locate/psychresns
Whole cortical and default mode network mean functional connectivity as potential biomarkers for mild Alzheimer's disease Marcio Luiz Figueredo Balthazar a,n, Brunno Machado de Campos a, Alexandre Rosa Franco b, Benito Pereira Damasceno a, Fernando Cendes a a b
Department of Neurology, University of Campinas (Unicamp), Sao Paulo 13083-970, Brazil Brain Institute of Rio Grande do Sul, Catholic University of Rio Grande do Sul (PUCRS), Porto Alegre, Brazil
art ic l e i nf o
a b s t r a c t
Article history: Received 24 April 2013 Received in revised form 6 August 2013 Accepted 31 October 2013 Available online 11 November 2013
The search for an Alzheimer's disease (AD) biomarker is one of the most relevant contemporary research topics due to the high prevalence and social costs of the disease. Functional connectivity (FC) of the default mode network (DMN) is a plausible candidate for such a biomarker. We evaluated 22 patients with mild AD and 26 age- and gender-matched healthy controls. All subjects underwent resting functional magnetic resonance imaging (fMRI) in a 3.0 T scanner. To identify the DMN, seed-based FC of the posterior cingulate was calculated. We also measured the sensitivity/specificity of the method, and verified a correlation with cognitive performance. We found a significant difference between patients with mild AD and controls in average z-scores: DMN, whole cortical positive (WCP) and absolute values. DMN individual values showed a sensitivity of 77.3% and specificity of 70%. DMN and WCP values were correlated to global cognition and episodic memory performance. We showed that individual measures of DMN connectivity could be considered a promising method to differentiate AD, even at an early phase, from normal aging. Further studies with larger numbers of participants, as well as validation of normal values, are needed for more definitive conclusions. & 2013 Elsevier Ireland Ltd. All rights reserved.
Keywords: Default mode network Biomarker Resting state fMRI Dementia
1. Introduction The search for an Alzheimer's disease (AD) biomarker is one of the most relevant research topics of the decade, largely due to the prevalence and social cost of the disease. A number of studies using biological fluids (such as cerebrospinal and plasma) as well as structural, functional, and molecular neuroimaging, are being conducted to improve the accuracy of AD diagnosis. Functional neuroimaging methods, as exemplified by functional magnetic resonance imaging (fMRI), have promise as biomarkers for AD, especially those obtained under resting conditions or without a specific task (“resting-state” fMRI, fc-fMRI or intrinsic brain activity). These methods have been helpful in identifying different functional connectivity networks (FC) through interregional correlation analysis of spontaneous fluctuations in the blood oxygenation level-dependent (BOLD) signal. Such studies have shown that synchronous oscillations in the BOLD signal, usually at a low frequency (0.01 to 0.1 Hz), represent functional neural networks
n Correspondence to: Department of Neurology, FCM, University of Campinas (UNICAMP), Cidade Universitaria, Campinas-SP, Brazil, 13083-970. Tel.: þ 55 19 3521 9217. E-mail addresses: [email protected], [email protected] (M.L.F. Balthazar).
0925-4927/$ - see front matter & 2013 Elsevier Ireland Ltd. All rights reserved. http://dx.doi.org/10.1016/j.pscychresns.2013.10.010
that are spatially distinct but functionally connected (Van den Heuvel and Hulshoff Pol, 2010). The main theoretical assumption for investigating FC disruption as a potential biomarker is that neurodegenerative diseases like AD are among those that can damage the neurofunctional organization of mental activity, leading to both cognitive and psychiatric symptoms. Seeley et al. (2009) demonstrated the network degeneration hypothesis across five distinct dementia syndromes, including AD. In typical AD, the progression of symptoms occurs in a stereotyped order, relating to the topographic progression of pathology: episodic memory loss takes place first (hippocampus and medial temporal lobe, along with posterior cingulate cortex), followed by problems with language and semantic memory (lateral temporal cortex) and visuospatial skills (temporal, and parietal neocortex). This orderly progression might be indicative of degeneration throughout interconnected regions within large-scale networks, which will ultimately spread into adjacent regions (Pievani et al., 2011). The default mode network (DMN) is the most studied network in AD. The function of the DMN is not completely understood, but it is accepted that there is increased activity when the patient is not focused on activities directed at the external environment (e.g., when an individual recalls autobiographical facts and events, or plans for the future). Some AD studies have shown a breakdown
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in functional connectivity in the DMN even at early stages of the disease (Greicius et al., 2004; Zhang et al., 2010; Jones et al., 2011; Prvulovic et al., 2011), but there are no well-established methods to detect this alteration on an individual basis. In this study, we investigated the relevance of the DMN and whole cortical mean functional connectivity as potential biomarkers in mild AD, through a seed-based approach in the posterior cingulate cortex—which is a well-established DMN anatomical region and known as one of the central hubs of the network. We looked at whether whole cortical mean connectivity in relation to posterior cingulate cortex, as well as DMN mean connectivity, might differentiate AD patients from age-matched healthy comparison subjects at the group and individual level. We also verified if the DMN and whole cortical connectivity were related to cognition. 2. Methods 2.1. Subjects Forty-eight subjects over age 50 were enrolled in this study, of whom 22 showed mild AD (16 women), and were followed at the Neuropsychology and Dementia Outpatient Clinic (UNICAMP University Hospital). For comparison, there were 26 healthy age- and gender-matched control subjects (20 women). Routine laboratory studies were performed on all patients, including B12 levels, folate levels, syphilis serology, and thyroid hormone levels. The study was approved by the Medical Research Ethics Committee of Medical Sciences School—UNICAMP. Local ethical committee approval and written informed consent (either from the subjects or from their responsible guardians if incapable) were obtained before study initiation, according to the Declaration of Helsinki. The diagnosis of probable AD was based on criteria established by the National Institute of Neurological and Communicative Disorders and the Stroke and the Alzheimer's Disease and Related Disorders Association (NINCDS/ADRDA) (McKhann et al., 1984). We only included patients who were classified with a clinical dementia rating (CDR)¼ 1. The exclusion criteria included a history of other neurological or psychiatric diseases, previous head injury with loss of consciousness, drug or alcohol addiction, prior chronic exposure to neurotoxic substances, and a Hachinski ischaemic score44. The control group consisted of subjects who were classified as CDR¼ 0 and had no history of neurologic disease, psychiatric disease, or memory complaints. 2.2. Neuropsychological, neuropsychiatric, and functional evaluations Global cognitive status was assessed with the Mini-Mental State Examination (MMSE) (Folstein et al., 1975; Brucki et al., 2003). We evaluated episodic memory with the Rey Auditory Verbal Learning Test (RAVLT) (Rey, 1964), and visual perception with subtests of Luria's Neuropsychological Investigation (LNI) (Christensen, 1975). Mental rotation was assessed using four items from the Ratcliff Manikin Test (Ratcliff, 1979). Evaluation of language included the Boston Naming Test (BNT) (Kaplan et al., 1983), verbal fluency for category (animals) and phonological awareness with the Foreign Accent Syndrome (FAS). We assessed constructive praxis with the Rey–Osterrieth complex figure test (ROCF) (Osterrieth, 1944), executive function with the trail making test (TMT) A and B, the stroop colorword test (SCWT), and the clock drawing test (CDT), and working memory with the forward (FDS) and backward digit span (BDS) subtest of the Wechsler adult intelligence scale (WAIS-R) (Wechsler, 1987). We applied the CDR with a semistructured interview (Morris, 1993). Data analysis was performed using SPSS software (version 20). We performed a t-test for intergroup comparisons of demographic and cognitive scores. When the data violated the assumptions of parametric tests, we performed the Mann– Whitney test. The results were considered to be statistically significant when p o0.05. 2.3. MRI acquisition Structural and functional images were acquired on a 3 T MRI scanner (Philips Achieva, Best, Netherlands). A set of structural images was acquired with the following sequences: (a) sagittal high-resolution T1-weighted with gradient echo images, repetition time/echo time (TR/TE)¼ 7/3.2 ms, field of view (FOV) ¼ 240 240, and isotropic voxels of 1 mm3; (b) coronal and axial FLAIR (Fluidattenuated Inversion Recovery) T2-weighted images, anatomically aligned at the hippocampus with image parameters set to TR/TE/TI (inversion time) ¼ 12000/140/ 2850, FOV ¼ 220 206, voxels reconstructed to 0.45 0.45 4.00 mm3 and gap between slices set to 1 mm; (c) coronal IR (inversion recovery) T1-weighted images with TR/TE/TI¼ 3550/15/400, FOV ¼180 180 and voxels reconstructed to
0.42 0.42 3.00 mm3; and (d) coronal multi-echo (5 echoes) T2-weighted images with TR/TE ¼3300/30, FOV ¼ 180 180, voxels reconstructed to 0.42 0.42 3.00 mm3. All images were reviewed to exclude other associated pathologies. Functional images were acquired under resting conditions. Subjects were instructed to keep their eyes closed and not to think of anything in particular. Axial T2n-weighted images had TR/TE ¼2000/30 ms, FOV ¼240 240, and isotropic voxels set to 3 3 3 mm3. For each participant, we acquired 10-min Echo Planar Images (EPI) and discarded the first three volumes to avoid T1 effects. The total of all remaining images corresponds to 300 volumes with 40 axial slices each. The participants’ sensory stimulation was limited to noise of the scanner during image acquisition. The subjects used earplugs to reduce this noise. In addition, all subjects had their head movements restricted by a soft Velcro strap. 2.4. Functional imaging analyses Functional images were initially preprocessed by applying slice-time and motion-correction algorithms. Linear trends were also removed from these data. Data pre-processing also included smoothing with a 6-mm FWHM (full width at half-maximum) Gaussian kernel and bandpass filtering (0.008 to 0.1 Hz). For spatial normalization, structural images where first linearly registered to the Montreal Neurological Institute (MNI)152 (standard space) with a 12-parameter affine transformation. The resulting image was again registered to the MNI152 image, now using a non-linear warping algorithm. Functional data were first registered to the structural image using a six-parameter affine transformation, then warped to standard space using the transformations calculated for the structural image. Six parameters of head motion, as well as cerebrospinal fluid and white matter time series, were regressed as nuisance variables. To identify the DMN, seed-based functional connectivity was calculated by placing a seed in the posterior cingulate cortex (0, 51, 15; MNI; seed radius¼ 3 mm). Specifically, for each subject, the average time course of voxels within this seed was extracted and generated a reference time series. Each time series was then correlated with all the voxels within the brain for each subject. Subsequently, r-scores of each voxel were then transformed using Fisher's r-to-z method, so that these data could be used in parametric statistical analysis to obtain whole cortical statistical z-score maps. All steps were performed with AFNI (http://afni.nimh.nih.gov/afni) and FSL (http://fsl. fmrib.ox.ac.uk/fsl/fslwiki/) software. We calculated an average z-score for each subject, considering (1) whole cortical absolute z-average (WCA), (2) whole cortical positive z-average (WCP), and (3) DMN positive z-average. To calculate the DMN z-average, we used a mask of DMN based on our control subjects’ statistical maps (z-score). These images were created according to the study methodology, with a seed placed on the posterior cingulate cortex (PCC). All maps were used to create an average image that was smoothed (FWHM ¼6 6 6 mm3) and binarized using a minimum threshold of 0.3 (z-score value). We compared the results between groups by using an independent two-sample t-test. We also calculated the sensitivity and specificity of the method, including a ROC (receiver operating characteristic) curve analysis. In addition, we verified if there was a significant correlation between DMN and WCP z-scores with global cognition (as measured by the MMSE) and episodic memory (as measured by RAVLT encoding, delayed recall, and recognition) to evaluate whether connectivity measures were related to cognition. We also performed a simple linear regression, while looking at AD and normal aging groups together. The results were assessed as statistically significant when P o0.05.
3. Results Table 1 presents demographic and cognitive results. There was no difference between patients and controls for gender and age, although controls had higher educational levels. The MMSE average score of our patients was 18.86, but we still considered them as mild AD for two main reasons: (1) Our elderly population had few years of formal education, especially among our public hospital patients (mean: 5.95 years, Table 1). In this study, three subjects were illiterate. Brucki et al. (2003) suggested that the normal MMSE values for the Brazilian population could be 20 for illiterates and 25 for those with 1 to 4 years of education. (2) We included only patients with CDR 1, and the mean CDR sum of boxes was 4.97. O'Bryant et al. (2008) showed that the optimal range of CDR-sum of boxes scores for a global score of 1.0 (mild) was 4.5 to 9.0. We found a significant statistical difference between mild AD and controls considering DMN's positive z average (t¼3.504; df¼46; P ¼0.001), WCP (positive z average, t¼2.934, df ¼46; P¼ 0.005), and WCA z-score values (t ¼2.567; df ¼46; P ¼0.01) (Fig. 1).
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Table 1 Demographic, functional and neuropsychological data.
Age (years) Education (years) Mini-mental status examination Clinical dementia rating—sum of boxes RAVLT—Encoding RAVLT—Delayed Recall RAVLT—Recognition Boston naming test (0–60) Semantic verbal fluency Phonologic verbal fluency (FAS) Visuospatial skills—LNI Forward digit span Backward digit span Trail making test-B (seconds) Stroop test- congruent (seconds) Stroop test- congruent (errors) Stroop test- incongruent (seconds) Stroop test-incongruent (errors) Clock drawing test (0–10) Rey complex figure (copy)
AD
Controls
p
73.40 7 5.67 5.95 7 5.17 18.86 7 4.68 4.977 0.87 18.137 6.75 0.50 7 0.67 3.85 7 4.89 29.53 7 13.80 9.317 4.78 18.42 7 12.51 14.727 2.93 3.777 1.47 1.727 1.27 278.737 56.60 79.42 7 43.75 0.477 0.92 179.95 7 78.59 33.527 21.98 5.09 7 2.81 13.24.04 7 14.57
71.03 7 6.61 10.22 7 5.55 28.59 7 1.86 0 47.54 78.06 9.047 2.41 11.95 7 2.53 52.047 4.93 17.63 74.99 33.27 711.72 18.09 7 1.19 5.137 1.88 4.187 1.33 138.047 95.49 56.047 17.60 0.047 0.21 102.09 7 27.35 2.86 7 3.73 9.40 7 1.50 34.88 7 3.81
0.19n o 0.05 o 0.0001 o 0.0001 o 0.0001 o 0.0001 o 0.0001 o 0.0001n o 0.0001 o 0.0001 0.01n 0.001n o 0.0001 0.001 0.62 o 0.0001n o 0.0001 0.0001 o 0.0001
Neuropsychological tests were performed in all subjects. Data presented as mean 7 SD. RAVLT: Rey auditory verbal learning test; LNI: Luria's Neuropsychological Investigation. n
Mann Whitney test was applied due lack of normality.
Fig. 1. Box-and-whiskers plot showing mean connectivity z scores: (A) Default mode network positive z average (DMN): mild AD (0.23 7 0.06) and controls (0.29 7 0.05). (B) Whole cortical positive z average (WCP): mild AD (0177 0.04) and controls (0.217 0.03). The box extends from the 25th percentile to the 75th percentile, with a horizontal line at the median. Whiskers extend down to the smallest value and up to the largest.
For DMN individual values, we found a sensitivity of 77.3% and a specificity of 70%, when using a cutoff z-score of 0.267. Area under the ROC curve is 0.767 (Fig. 2A). For WCP individual values, we found a sensitivity of 72.7% and a specificity of 70%, when using a cutoff z-score of 0.195. Area under the ROC curve is 0.731, and the standard error is 0.67 (Fig. 2B). In addition, when considering the two groups together, we found a significant correlation with DMN z-scores and MMSE score (r ¼ 0.45, P ¼0.001) and with RAVLT encoding (r ¼0.34, P ¼0.01), delayed recall (A7; r ¼ 0.32, P ¼0.02), and recognition (r ¼0.43; P ¼0.002) (Fig. 3). WCP z-scores were significantly correlated with MMSE scores (r ¼0.40; P ¼0.004) and RAVLT recognition (r ¼0.36, P ¼0.006) (Fig. 4). None of the other possible correlations with other neuropsychological tests reached statistical significance. Furthermore, when we performed these correlations only in the AD group, no statistically significant results were obtained.
4. Discussion We have shown that individual measures of DMN, as well as whole cortical connectivity in relation to DMN in the posterior cingulate cortex, can be considered a promising method to differentiate AD from normal aging, even in its early phases. At a group level, we found a significant difference between patients and controls (P¼ 0.001 for DMN and P¼ 0.005 for the WCP). At an individual level, our findings were less substantial, but also noteworthy: we found a sensitivity of 77.3% and a specificity of 70% when we considered the mean DMN connectivity (area under curve is 76.7%). In addition, we found that these connectivity measures were related to global cognition and episodic memory performance. DMN positive z-scores were superior to WCP in differentiating groups and individuals, and were most significantly correlated with cognition.
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Fig. 2. ROC curves in DMN and WCP.
Fig. 3. Simple linear regressions between DMN z scores and cognition. (A) DMN MMSE; (B) DMN RAVLT's encoding; (C) DMN RAVLT's delayed recall; (D) DMN RAVLT's recognition.
The cerebral structure of cognitive functions is organized in several functional networks, which dynamically interact with each other. Neurodegenerative diseases like AD, however, disrupt this organization and cause cognitive and psychiatric symptoms. Alterations in DMN functional connectivity have been increasingly recognized in AD, as this FC represents a characteristic set of brain
regions, which are among the first to show abnormal amyloid deposition (a key pathological feature of AD). This includes areas like the precuneus, posterior cingulate cortex, medial prefrontal cortex, lateral temporal and parietal cortices, and hippocampus (Sheline and Raichle, 2013). Drzezga et al. (2011) reported a relationship of global functional connectivity with amyloid
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Fig. 4. Simple linear regressions between WCP z scores and cognition. (A) WCP MMSE; (B) WCP RAVLT's recognition.
depositions and hypometabolism in the precuneus and posterior cingulate cortex, even in the healthy elderly; they used positron emission tomography (PET) with Pittsburgh compound-B (PETPIB) (a sensitive marker of amyloid pathology in patients with AD). They hypothesized that the emerging amyloid load could initiate the synaptic dysfunction, which causes disruption in the functional connectivity in these regions. Adriaanse et al. (2012) also found reduced DMN connectivity in subjects with AD, but did not find an association of functional connectivity in the DMN with amyloid load. Some authors showed an association between lower DMN deactivation years before clinical symptoms of AD appeared (and a spontaneous neuronal activation higher than expected) and the amyloid load (Buckner et al., 2008). According to this theory (“metabolic hypothesis”), functional alterations in DMN could precede the pathological changes that typically occur in AD. The metabolic hypothesis is supported by recent studies, demonstrating that functional alterations precede atrophy in the posterior cingulate cortex (Gili et al., 2011), and that asymptomatic carriers of Apolipoprotein ε4 (ApoE-4) have a connectivity reduction in the posterior cingulate cortex (Machulda et al., 2011). Several authors found differences in DMN connectivity between AD and normal aging at a group level; however, there are fewer studies that examined differences on an individual level (Greicius et al., 2004; Wang et al., 2007; Zhang et al., 2010; Drzezga et al., 2011; Pievani et al., 2011; Adriaanse et al., 2012; Koch et al., 2012). Different fMRI methods have been used for this purpose. Greicius et al. (2004) evaluated individual measures of DMN integrity to separate AD from normal aging in an indirect way: they compared the fit of a subject's DMN to a standard default mode template. These authors also used another method to obtain the DMN (independent component analysis - ICA) in 13 patients and 13 controls, and found a sensitivity of 85% and a specificity of 77% to differentiate these subjects. In a recent study, Koch et al. (2012) combined two approaches: ICA- and seed-based interconnectivity analyses. While each approach revealed moderate results when used separately, a combination of both methods (within a multivariate approach) showed a sensitivity of 100% and specificity of 95% in discriminating AD patients from healthy subjects. Supekar et al. (2008) demonstrated a loss of smallworld properties in AD patients, characterized by a significantly lower clustering coefficient; this was most apparent in the left and right hippocampus, which indicated disrupted local connectivity. The clustering coefficient distinguished AD participants from the controls, with a sensitivity of 72% and specificity of 78%. DMN disruptions can also be related to cognitive problems in AD. Their exact role is not known, but there are some hypotheses
about its function. As discussed by Buckner et al. (2008), DMN can be associated with spontaneous cognition, in which human thought is separate from the immediate environment. Its activation is characterized by mental exploration based on personal introspection, autobiographical memories, and thoughts about the future. In the healthy brain, greater suppression of the DMN is associated with better memory formation (Daselaar et al., 2009). Other authors have reported a correlation between memory and DMN; Celone et al. (2006) found associations between DMN connectivity and memory performance. Westlye et al. (2011) showed a negative correlation between DMN synchronization and performance on memory tests in healthy elderly individual carriers of ApoE-4, a gene associated with an increased risk factor for sporadic AD. Binnewijzend et al. (2012) also found an association between cognition and functional connectivity measures in DMN, including the MMSE and episodic memory. Our results emphasized the relevance of DMN connectivity in cognition, specifically concerning episodic memory. These findings are in accordance with those of Seeley et al. (2009): their hypothesis suggested that different neurodegenerative syndromes affect different FCs, and thus cause different cognitive and behavioral profiles. In this view, AD could be considered as having a specific DMN problem in its initial phase, while also causing early episodic memory dysfunction. Resting state fMRI has many advantages in the study of AD: its noninvasive nature, the short time for imaging acquisition (6 to 10 min), and the fact that it can be done without any specific task: AD patients may be too impaired to actively participate in a taskbased scanning paradigm (Sheline and Raichle, 2013). In addition, analyses of this seed-based method are relatively simple and can be fully automated. Our study did not reach sensitivity/specificity greater than 80%, but the DMN findings represent a measure that could be a clinically relevant biomarker for AD. Functional connectivity as assessed by fMRI is a relatively new research field, and technical advances in the acquisition and analyses of data are increasingly being studied. For example, Boyacioglu and Barth (2012) described a new fMRI technique (ultrafast fMRIGeneralized iNverse imaging-GIN), which increases temporal resolution (50 ms) without losing spatial resolution. Increasing the magnetic field may also be useful in fMRI studies in the future. De Martino et al. (2011) demonstrated that high-resolution images acquired at 7 T might allow for separation of detailed spatial features within the DMN, including a better correlation with anatomy. Although we know that functional connectivity measures may be influenced by atrophy, as we discussed in a recent article
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(Balthazar et al., 2013), we believe that correcting fMRI results for atrophy is not necessarily useful in the context of biomarker research, when the main goal is just to differentiate patients and normal subjects. Our work should encourage further longitudinal studies examining mean DMN and WCP z-scores, connectivity measures in AD, and mild cognitive impairment. Increasing the sample of control subjects, selecting specific areas of DMN rather than the whole network, and evaluating the data in native space might improve the statistical strength of this method.
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