Structural Neuroimaging Markers of Cognitive Decline in Parkinson's Disease
Identifieur interne : 000176 ( Pmc/Corpus ); précédent : 000175; suivant : 000177Structural Neuroimaging Markers of Cognitive Decline in Parkinson's Disease
Auteurs : Alexandru Hanganu ; Oury MonchiSource :
- Parkinson's Disease [ 2090-8083 ] ; 2016.
Abstract
Cognitive impairment in patients with Parkinson's disease is a major challenge since it has been established that 25 to 40% of patients will develop cognitive impairment early in the disease. Furthermore, it has been reported that up to 80% of Parkinsonian patients will eventually develop dementia. Thus, it is important to improve the diagnosing procedures in order to detect cognitive impairment at early stages of development and to delay as much as possible the developing of dementia. One major challenge is that patients with mild cognitive impairment exhibit measurable cognitive deficits according to recently established criteria, yet those deficits are not severe enough to interfere with daily living, hence being avoided by patients, and might be overseen by clinicians. Recent advances in neuroimaging brain analysis allowed the establishment of several anatomical markers that have the potential to be considered for early detection of cognitive impairment in Parkinsonian patients. This review aims to outline the neuroimaging possibilities in diagnosing cognitive impairment in patients with Parkinson's disease and to take into consideration the near-future possibilities of their implementation into clinical practice.
Url:
DOI: 10.1155/2016/3217960
PubMed: 27190672
PubMed Central: 4848447
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PMC:4848447Le document en format XML
<record><TEI><teiHeader><fileDesc><titleStmt><title xml:lang="en">Structural Neuroimaging Markers of Cognitive Decline in Parkinson's Disease</title><author><name sortKey="Hanganu, Alexandru" sort="Hanganu, Alexandru" uniqKey="Hanganu A" first="Alexandru" last="Hanganu">Alexandru Hanganu</name><affiliation><nlm:aff id="I1">Department of Clinical Neurosciences and Department of Radiology, University of Calgary, Calgary, AB, Canada T2N 1N4</nlm:aff></affiliation><affiliation><nlm:aff id="I2">Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada T2N 4N1</nlm:aff></affiliation></author><author><name sortKey="Monchi, Oury" sort="Monchi, Oury" uniqKey="Monchi O" first="Oury" last="Monchi">Oury Monchi</name><affiliation><nlm:aff id="I1">Department of Clinical Neurosciences and Department of Radiology, University of Calgary, Calgary, AB, Canada T2N 1N4</nlm:aff></affiliation><affiliation><nlm:aff id="I2">Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada T2N 4N1</nlm:aff></affiliation><affiliation><nlm:aff id="I3">Centre de Recherche, Institut Universitaire de Gériatrie de Montréal, Université de Montréal, Montréal, QC, Canada H3W 1W5</nlm:aff></affiliation><affiliation><nlm:aff id="I4">McGill University, Montreal, QC, Canada H3A 0G4</nlm:aff></affiliation><affiliation><nlm:aff id="I5">Department of Radiology, Faculty of Medicine, University of Montreal, Montreal, QC, Canada H3T 1J4</nlm:aff></affiliation></author></titleStmt><publicationStmt><idno type="wicri:source">PMC</idno><idno type="pmid">27190672</idno><idno type="pmc">4848447</idno><idno type="url">http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4848447</idno><idno type="RBID">PMC:4848447</idno><idno type="doi">10.1155/2016/3217960</idno><date when="2016">2016</date><idno type="wicri:Area/Pmc/Corpus">000176</idno><idno type="wicri:explorRef" wicri:stream="Pmc" wicri:step="Corpus" wicri:corpus="PMC">000176</idno></publicationStmt><sourceDesc><biblStruct><analytic><title xml:lang="en" level="a" type="main">Structural Neuroimaging Markers of Cognitive Decline in Parkinson's Disease</title><author><name sortKey="Hanganu, Alexandru" sort="Hanganu, Alexandru" uniqKey="Hanganu A" first="Alexandru" last="Hanganu">Alexandru Hanganu</name><affiliation><nlm:aff id="I1">Department of Clinical Neurosciences and Department of Radiology, University of Calgary, Calgary, AB, Canada T2N 1N4</nlm:aff></affiliation><affiliation><nlm:aff id="I2">Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada T2N 4N1</nlm:aff></affiliation></author><author><name sortKey="Monchi, Oury" sort="Monchi, Oury" uniqKey="Monchi O" first="Oury" last="Monchi">Oury Monchi</name><affiliation><nlm:aff id="I1">Department of Clinical Neurosciences and Department of Radiology, University of Calgary, Calgary, AB, Canada T2N 1N4</nlm:aff></affiliation><affiliation><nlm:aff id="I2">Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada T2N 4N1</nlm:aff></affiliation><affiliation><nlm:aff id="I3">Centre de Recherche, Institut Universitaire de Gériatrie de Montréal, Université de Montréal, Montréal, QC, Canada H3W 1W5</nlm:aff></affiliation><affiliation><nlm:aff id="I4">McGill University, Montreal, QC, Canada H3A 0G4</nlm:aff></affiliation><affiliation><nlm:aff id="I5">Department of Radiology, Faculty of Medicine, University of Montreal, Montreal, QC, Canada H3T 1J4</nlm:aff></affiliation></author></analytic><series><title level="j">Parkinson's Disease</title><idno type="ISSN">2090-8083</idno><idno type="eISSN">2042-0080</idno><imprint><date when="2016">2016</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en"><p>Cognitive impairment in patients with Parkinson's disease is a major challenge since it has been established that 25 to 40% of patients will develop cognitive impairment early in the disease. Furthermore, it has been reported that up to 80% of Parkinsonian patients will eventually develop dementia. Thus, it is important to improve the diagnosing procedures in order to detect cognitive impairment at early stages of development and to delay as much as possible the developing of dementia. One major challenge is that patients with mild cognitive impairment exhibit measurable cognitive deficits according to recently established criteria, yet those deficits are not severe enough to interfere with daily living, hence being avoided by patients, and might be overseen by clinicians. Recent advances in neuroimaging brain analysis allowed the establishment of several anatomical markers that have the potential to be considered for early detection of cognitive impairment in Parkinsonian patients. 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<front><journal-meta><journal-id journal-id-type="nlm-ta">Parkinsons Dis</journal-id><journal-id journal-id-type="iso-abbrev">Parkinsons Dis</journal-id><journal-id journal-id-type="publisher-id">PD</journal-id><journal-title-group><journal-title>Parkinson's Disease</journal-title></journal-title-group><issn pub-type="ppub">2090-8083</issn><issn pub-type="epub">2042-0080</issn><publisher><publisher-name>Hindawi Publishing Corporation</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type="pmid">27190672</article-id><article-id pub-id-type="pmc">4848447</article-id><article-id pub-id-type="doi">10.1155/2016/3217960</article-id><article-categories><subj-group subj-group-type="heading"><subject>Review Article</subject></subj-group></article-categories><title-group><article-title>Structural Neuroimaging Markers of Cognitive Decline in Parkinson's Disease</article-title></title-group><contrib-group><contrib contrib-type="author"><name><surname>Hanganu</surname><given-names>Alexandru</given-names></name><xref ref-type="aff" rid="I1"><sup>1</sup></xref><xref ref-type="aff" rid="I2"><sup>2</sup></xref><xref ref-type="corresp" rid="cor1"><sup>*</sup></xref></contrib><contrib contrib-type="author"><name><surname>Monchi</surname><given-names>Oury</given-names></name><xref ref-type="aff" rid="I1"><sup>1</sup></xref><xref ref-type="aff" rid="I2"><sup>2</sup></xref><xref ref-type="aff" rid="I3"><sup>3</sup></xref><xref ref-type="aff" rid="I4"><sup>4</sup></xref><xref ref-type="aff" rid="I5"><sup>5</sup></xref></contrib></contrib-group><aff id="I1"><sup>1</sup>Department of Clinical Neurosciences and Department of Radiology, University of Calgary, Calgary, AB, Canada T2N 1N4</aff><aff id="I2"><sup>2</sup>Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada T2N 4N1</aff><aff id="I3"><sup>3</sup>Centre de Recherche, Institut Universitaire de Gériatrie de Montréal, Université de Montréal, Montréal, QC, Canada H3W 1W5</aff><aff id="I4"><sup>4</sup>McGill University, Montreal, QC, Canada H3A 0G4</aff><aff id="I5"><sup>5</sup>Department of Radiology, Faculty of Medicine, University of Montreal, Montreal, QC, Canada H3T 1J4</aff><author-notes><corresp id="cor1">*Alexandru Hanganu: <email>alexandru.hanganu@ucalgary.ca</email></corresp><fn fn-type="other"><p>Academic Editor: Nicola Modugno</p></fn></author-notes><pub-date pub-type="ppub"><year>2016</year></pub-date><pub-date pub-type="epub"><day>14</day><month>4</month><year>2016</year></pub-date><volume>2016</volume><elocation-id>3217960</elocation-id><history><date date-type="received"><day>24</day><month>10</month><year>2015</year></date><date date-type="rev-recd"><day>28</day><month>1</month><year>2016</year></date><date date-type="accepted"><day>16</day><month>2</month><year>2016</year></date></history><permissions><copyright-statement>Copyright © 2016 A. Hanganu and O. Monchi.</copyright-statement><copyright-year>2016</copyright-year><license license-type="open-access"><license-p>This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.</license-p></license></permissions><abstract><p>Cognitive impairment in patients with Parkinson's disease is a major challenge since it has been established that 25 to 40% of patients will develop cognitive impairment early in the disease. Furthermore, it has been reported that up to 80% of Parkinsonian patients will eventually develop dementia. Thus, it is important to improve the diagnosing procedures in order to detect cognitive impairment at early stages of development and to delay as much as possible the developing of dementia. One major challenge is that patients with mild cognitive impairment exhibit measurable cognitive deficits according to recently established criteria, yet those deficits are not severe enough to interfere with daily living, hence being avoided by patients, and might be overseen by clinicians. Recent advances in neuroimaging brain analysis allowed the establishment of several anatomical markers that have the potential to be considered for early detection of cognitive impairment in Parkinsonian patients. This review aims to outline the neuroimaging possibilities in diagnosing cognitive impairment in patients with Parkinson's disease and to take into consideration the near-future possibilities of their implementation into clinical practice.</p></abstract></article-meta></front><body><sec id="sec1"><title>1. Introduction</title><p>Parkinson's disease (PD) is a movement disorder and the second most frequent chronic neurodegenerative disease, affecting up to 2 percent among persons older than 65 years [<xref rid="B1" ref-type="bibr">1</xref>] and nearly 10% of people older than 80 years [<xref rid="B2" ref-type="bibr">2</xref>]. Cognitive deficits were consistently reported in patients with PD for measures of executive function and working memory, suggesting dysfunctional frontostriatal brain circuitry [<xref rid="B3" ref-type="bibr">3</xref>–<xref rid="B5" ref-type="bibr">5</xref>]. It has been established that up to 40% of patients with PD present with mild cognitive impairment (MCI) early in the disease [<xref rid="B6" ref-type="bibr">6</xref>]. MCI is defined as a cognitive deficit commonly quantified as a performance level 1-2 SDs below the population mean in one or more cognitive domains [<xref rid="B7" ref-type="bibr">7</xref>]. Some studies suggested that PD patients with MCI (PD-MCI) have an increased risk of developing dementia compared with patients with normal cognition (PD-NC) [<xref rid="B8" ref-type="bibr">8</xref>–<xref rid="B10" ref-type="bibr">10</xref>]. A prospective study of 8-year follow-up reported that 78,2% of PD patients eventually develop dementia [<xref rid="B11" ref-type="bibr">11</xref>], and from the PD-MCI group up to 62% of patients converted to dementia over a 4-year period, compared with 20% of PD-NC patients [<xref rid="B10" ref-type="bibr">10</xref>, <xref rid="B12" ref-type="bibr">12</xref>, <xref rid="B13" ref-type="bibr">13</xref>]. Thus, avoiding dementia becomes a key part in preserving an active life for PD patients and early MCI diagnosis allows us to take the necessary steps for achieving that goal.</p><p>The diagnosis of MCI and dementia in PD remains still clinical and neuroimaging techniques can only be used as supportive measures. Several neuroimaging parameters have been described and can be used to analyze the changes in gray matter structure. Cortical thickness and cortical surface area have been proposed as useful measures to analyze the cortical gray matter morphology, as they have the advantage of providing a direct quantitative index [<xref rid="B14" ref-type="bibr">14</xref>, <xref rid="B15" ref-type="bibr">15</xref>]. A third parameter, the gray matter volume, can be measured in both cortical and subcortical structures. Cortical thickness is measured as the closest distance from the gray/white matter boundary to the gray/cerebrospinal fluid boundary. To measure the cortical surface area, the cortex is arranged in a triangular grid and the final value of the surface area is calculated by measuring the area of each triangle of the midsurface and assigning one-third of this area to each of its three vertices (i.e., the area assigned to each vertex is one-third of the total area of all triangular facets adjoining it). The cortical gray matter volume is a product of thickness and surface area [<xref rid="B16" ref-type="bibr">16</xref>] while the volumes of subcortical gray matter structures represent the volume of the three-dimensional space, based on the automatic or semimanually defined regions of interest.</p><p>Based upon the previous description that neurons within the cerebral cortex are organized into ontogenetic columns that run perpendicular to the brain surface [<xref rid="B17" ref-type="bibr">17</xref>], it has been suggested that measurement of cortical thickness is linked with the number of cells within a column while surface area relates directly to minicolumn number and spacing [<xref rid="B15" ref-type="bibr">15</xref>, <xref rid="B18" ref-type="bibr">18</xref>, <xref rid="B19" ref-type="bibr">19</xref>]. Thus, the concept of cortical thickness reflects the arrangement of neurons and neuropil in a biologically and topologically meaningful way; cortical surface area assesses the local cortical folding while the changes in gray matter volume may reflect changes in either thickness, surface area, or both [<xref rid="B15" ref-type="bibr">15</xref>, <xref rid="B16" ref-type="bibr">16</xref>, <xref rid="B19" ref-type="bibr">19</xref>]. Other studies suggested that gray matter volume correlates more with surface area, because the regional area is measured on the surface between adjacent landmarks, giving a higher quadratic weight to tangential (horizontal) than to vertical (radial) distances [<xref rid="B20" ref-type="bibr">20</xref>]. Due to this criterion, surface area tends to have more variability and thus gray matter volume also has greater variability [<xref rid="B20" ref-type="bibr">20</xref>], implying that gray matter volume-based measurement techniques might have a lower reliability compared with cortical thickness-based measurement techniques.</p><p>From a morphological point of view, cortical thickness does not directly relate to neuronal loss or neuronal density. It has been shown that over time some brain regions (lateral and mesial prefrontal and inferior parietal) maintain a relatively constant cortical thickness and neuronal density, whereas in other regions (all remaining brain regions) neuronal density linearly decreased with increased thickness [<xref rid="B21" ref-type="bibr">21</xref>]. Thus, lower cortical thickness does not necessarily mean neuronal loss, but rather loss of neuronal and dendritic architecture, for example, reduced size of neuronal cell body, reduced dendritic arborization, or the loss of presynaptic terminals [<xref rid="B22" ref-type="bibr">22</xref>]. Additionally, local surface area might reflect the state of the underlying white matter fibres, as more tension or shrinkage of these fibres could lead to deeper sulci and extended cortical surface area. Thus, cortical surface area can also indirectly reflect white matter tract damage [<xref rid="B23" ref-type="bibr">23</xref>].</p><p>Some current neuroimaging software programs have the potential to be used for automatic integration in a pipeline and assessment of gray matter measurements on an individual level. These include Freesurfer (<ext-link ext-link-type="uri" xlink:href="http://surfer.nmr.mgh.harvard.edu/">http://surfer.nmr.mgh.harvard.edu/</ext-link>) [<xref rid="B24" ref-type="bibr">24</xref>], voxel-based morphometry (VBM) analyses using the FMRIB Software Library (FSL) (<ext-link ext-link-type="uri" xlink:href="http://www.fmrib.ox.ac.uk/fsl/fslvbm">http://www.fmrib.ox.ac.uk/fsl/fslvbm</ext-link>) [<xref rid="B25" ref-type="bibr">25</xref>], Statistical Parametric Mapping (SPM) analyses in MATLAB (MathWorks, Inc., Natick, MA, USA) using the VBM toolbox (<ext-link ext-link-type="uri" xlink:href="http://www.fil.ion.ucl.ac.uk/spm/">http://www.fil.ion.ucl.ac.uk/spm/</ext-link>) [<xref rid="B26" ref-type="bibr">26</xref>], and the SurfStat toolbox (<ext-link ext-link-type="uri" xlink:href="http://www.math.mcgill.ca/keith/surfstat/">http://www.math.mcgill.ca/keith/surfstat/</ext-link>) [<xref rid="B27" ref-type="bibr">27</xref>] in MATLAB. This brings the possibility for defining brain structural markers for certain pathologies and for measuring the cortical parameters on an individual level, thus opening the opportunity for a quick estimation of the risk for developing the pathology and for assessing its progression over time. One additional approach to do this on individual level is to use automatic learning algorithms such as Support Vector Machines, which allow both the definitions of new patients' groups as well as single-patient classifications into those groups [<xref rid="B28" ref-type="bibr">28</xref>]. Considering that in a clinical setting the costs for performing a structural T1 magnetic resonance imaging (MRI) sequence are lower compared to a diffusion weighted image (DWI) and that analyzing the gray matter parameters of cortical thickness and cortical surface area deformation can be easily performed without manual interventions compared with analyses of white matter parameters which need manual adjustments at more stages, currently it seems more feasible to implement an automated pipeline based on structural gray matter changes. Thus, the present review will focus mainly on the brain structural markers of cognitive decline in patients with Parkinson's disease with emphasis on the gray matter markers.</p><p>Studies in the last decade analyzed the patterns of brain anatomical changes in Parkinsonian patients with and without dementia and in comparison to healthy controls. First, it is important to note that in several cross-sectional and longitudinal studies in which PD patients were distributed in groups based on their cognitive status, PD-NC patients did not demonstrate significant brain atrophy compared with healthy controls [<xref rid="B29" ref-type="bibr">29</xref>–<xref rid="B31" ref-type="bibr">31</xref>]. Second, PD patients with dementia (PDD) had significant anatomical changes compared with nondemented PD patients (NDPD, which includes both PD-NC and PD-MCI) and healthy controls. Burton et al. [<xref rid="B32" ref-type="bibr">32</xref>] reported that PDD have reduced gray matter volume compared to healthy controls in the hippocampus, parahippocampal gyrus, occipital lobe, right frontal lobe, and left parietal lobes. In addition, when compared with NDPD patients, PDD had more gray matter atrophy in the occipital lobe, suggesting that in PDD the atrophy pattern extends to temporal, occipital, and subcortical areas, but occipital atrophy being the only difference between the groups. By contrast, other groups that analyzed the gray matter volume changes between PDD and PD using VBM reported significant differences only in the left superior temporal gyrus and right hippocampus [<xref rid="B33" ref-type="bibr">33</xref>] or in the dorsolateral prefrontal cortex (DLPFC), anterior cingulate gyrus, temporal lobe, and subcortical regions, hippocampus, thalamus, and caudate nucleus [<xref rid="B34" ref-type="bibr">34</xref>]. In continuation to this pattern, another VBM study examined the gray matter changes in PDD compared with PD-NC and PD-MCI patients. Results showed gray matter reductions in PD-MCI patients in the left middle frontal gyrus, precentral gyrus, left superior temporal lobe, and right inferior temporal lobe compared to PD-NC patients, while the PDD group was reported to have reduced gray matter volume in the bilateral frontal, temporal, parietal lobes and the limbic region (medial occipital) compared to PD-NC and PD-MCI combined [<xref rid="B35" ref-type="bibr">35</xref>]. Our group analyzed the cortical changes over time and reported a higher rate of cortical thinning in PD-MCI patients in the supplementary motor area (SMA), superior temporal gyrus, superior parietal region, and medial occipital cortex [<xref rid="B29" ref-type="bibr">29</xref>]. A recent longitudinal study on a bigger group of Parkinsonian patients reported that PD-MCI and PD-NC did not have significant differences in regional cortical thickness at baseline, while after 18 months the PD-MCI group demonstrated widespread cortical thinning in the bilateral pre-SMA, right SMA, and left superior temporal gyrus in comparison with PD-NC patients [<xref rid="B30" ref-type="bibr">30</xref>]. Interestingly both longitudinal studies reported the atrophy of nucleus accumbens in PD-MCI patients in comparison with PD-NC as well as healthy controls [<xref rid="B29" ref-type="bibr">29</xref>, <xref rid="B30" ref-type="bibr">30</xref>]. Furthermore, PD-MCI patients showed a correlation between the Montreal Cognitive Assessment scale scores and cortical thinning in the left postcentral gyrus, middle and inferior temporal gyri, and right fusiform gyrus in one study [<xref rid="B29" ref-type="bibr">29</xref>], as well as left superior frontal, orbitofrontal, inferior parietal cortices and left fusiform and right parahippocampal gyri in another study [<xref rid="B30" ref-type="bibr">30</xref>]. Hence, several cortical and subcortical regions might have the potential to be considered as markers of cognitive impairment associated neurodegeneration.</p></sec><sec id="sec2"><title>2. Frontal Lobe</title><p>SMA and pre-SMA are the primary potential candidates as structural neuroimaging markers specific for MCI in PD patients (<xref ref-type="fig" rid="fig1">Figure 1</xref>, clusters 6 and 7). Bilateral SMA and pre-SMA were shown to have faster rates of cortical thinning over time in PD-MCI patients compared with PD-NC and healthy controls in longitudinal studies [<xref rid="B29" ref-type="bibr">29</xref>, <xref rid="B30" ref-type="bibr">30</xref>]. Cross-sectional studies revealed structural gray and white matter changes in PD when compared with healthy controls. PD-MCI patients were shown to have cortical thinning [<xref rid="B39" ref-type="bibr">36</xref>] and decreased fractional anisotropy (a diffusion imaging measure thought to reflect fiber density, axonal diameter, and myelination) in the anterior superior longitudinal fasciculus (which connects the SMA and DLPFC with superior and medial parietal cortex) [<xref rid="B40" ref-type="bibr">37</xref>] while NDPD patients were reported to have SMA cortical gray matter thinning [<xref rid="B41" ref-type="bibr">38</xref>], decreased white matter fractional anisotropy [<xref rid="B42" ref-type="bibr">39</xref>], and connectivity changes (the SMA-cerebellum connection was inhibitory in PD and excitatory in healthy controls [<xref rid="B43" ref-type="bibr">40</xref>]). Poorer backward digit span test of the Wechsler Adult Intelligence Scale (a measure of short-term memory) was shown to correlate with lower fractional anisotropy in the paracentral lobule (the anterior portion of which refers to SMA) [<xref rid="B44" ref-type="bibr">41</xref>]. Pre-SMA showed hypometabolism associated with cognitive deficits [<xref rid="B45" ref-type="bibr">42</xref>]. Moreover, in our previous study in NDPD, cortical thinning and area enlargement in SMA showed significant positive correlation with duration of disease [<xref rid="B46" ref-type="bibr">43</xref>] while another pathological study has disclosed significant loss of corticocortical projecting pyramidal neurons in the pre-SMA without intraneuronal inclusions [<xref rid="B47" ref-type="bibr">44</xref>]. The results of the later studies might have been driven by the PD-MCI group.</p><p>Other frontal clusters were also reported. Specifically, cross-sectional analysis showed reduced gray matter volume in PDD compared with NDPD in the frontal lobes bilaterally [<xref rid="B35" ref-type="bibr">35</xref>] and PDD versus healthy controls had gray matter atrophic changes in the DLPFC [<xref rid="B34" ref-type="bibr">34</xref>] and anterior cingulate [<xref rid="B33" ref-type="bibr">33</xref>, <xref rid="B34" ref-type="bibr">34</xref>]. Other studies reported gray matter reductions in PD-MCI in the middle frontal gyrus and precentral gyrus compared with PD-NC patients [<xref rid="B35" ref-type="bibr">35</xref>] and diminished cortical thickness in the ventromedial prefrontal cortex (VMPFC) and premotor cortices when compared with healthy controls [<xref rid="B39" ref-type="bibr">36</xref>, <xref rid="B46" ref-type="bibr">43</xref>]. NDPD patients had reduced gray matter volume compared with healthy controls in the superior, middle, and inferior frontal gyri [<xref rid="B32" ref-type="bibr">32</xref>]. Longitudinal analyses in PD-MCI compared with healthy controls reported cortical atrophy in the VMPFC, DLPFC, and premotor cortices [<xref rid="B29" ref-type="bibr">29</xref>, <xref rid="B30" ref-type="bibr">30</xref>]. On the other hand, several studies reported no significant differences in the DLPFC, VMPFC, and anterior cingulate between NDPD and healthy controls [<xref rid="B33" ref-type="bibr">33</xref>, <xref rid="B34" ref-type="bibr">34</xref>] or NDPD and PDD [<xref rid="B33" ref-type="bibr">33</xref>]. The distinct results can be due to different techniques for measuring the gray matter changes, different smoothing thresholds, different methods of creating the average subject for the groups, and different methods of accounting for confounding factors such as age, disease duration, education, and levodopa dosages [<xref rid="B48" ref-type="bibr">45</xref>].</p><p>The presence of some of these frontal clusters is in line with previously described impairment in the frontostriatal brain circuitry in PD patients [<xref rid="B3" ref-type="bibr">3</xref>–<xref rid="B5" ref-type="bibr">5</xref>, <xref rid="B49" ref-type="bibr">46</xref>–<xref rid="B51" ref-type="bibr">48</xref>] according to which basal ganglia control the functioning of the frontal regions [<xref rid="B52" ref-type="bibr">49</xref>]. Two of these circuits include the lateral orbitofrontal cortex (part of the VMPFC) and the “motor circuit” that is primarily directed to the precentral motor fields [<xref rid="B53" ref-type="bibr">50</xref>, <xref rid="B54" ref-type="bibr">51</xref>]. Lesioned prefrontal cortex [<xref rid="B55" ref-type="bibr">52</xref>] and lesions of the VMPFC induce the inability to weigh cost versus benefits in the Iowa gambling task [<xref rid="B56" ref-type="bibr">53</xref>, <xref rid="B57" ref-type="bibr">54</xref>] and can cause profound changes in emotional and social behaviours, including impairments in certain aspects of decision making [<xref rid="B58" ref-type="bibr">55</xref>, <xref rid="B59" ref-type="bibr">56</xref>]. Such functional impairments have been described in PD patients; they are impaired at making choices that require learning from trial and error [<xref rid="B60" ref-type="bibr">57</xref>, <xref rid="B61" ref-type="bibr">58</xref>] and in decision making during explicit gambling situations [<xref rid="B62" ref-type="bibr">59</xref>]. However, some frontal clusters are reported by some studies but not by others and tend to appear in the contrasts that compare PD patients with healthy controls but do not appear in the contrasts between different cognitive profiles of PD (PDD versus NDPD, PDD versus PD-MCI, and PD-MCI versus PD-NC). Hence, we can distinguish three groups of clusters in the frontal lobe: (1) clusters that have structural changes due to the presence of PD and have little associations with cognitive impairment (the motor and premotor cortices), (2) clusters that show some association with cognitive impairment but can be considered reliable only with the presence of additional neuropsychological testing (DLPFC, VMPFC) (<xref ref-type="fig" rid="fig1">Figure 1</xref>, clusters 4 and 5), (3) and clusters that seem to be associated directly with cognitive impairment and can be reliable structural markers that indicate the presence of cortical changes due to cognitive impairment in PD (SMA and pre-SMA).</p></sec><sec id="sec3"><title>3. Temporal Lobe</title><p>A large number of studies reported temporal lobe changes associated with MCI in PD patients. Cross-sectional studies reported cortical atrophy in PD-MCI [<xref rid="B39" ref-type="bibr">36</xref>] as well as PDD patients [<xref rid="B32" ref-type="bibr">32</xref>]. Specifically, PD-MCI were reported to have changes in the medial temporal lobe with thinner parahippocampal and fusiform cortices [<xref rid="B31" ref-type="bibr">31</xref>, <xref rid="B63" ref-type="bibr">60</xref>] as well as increased surface area [<xref rid="B46" ref-type="bibr">43</xref>] when compared to PD-NC and greater cortical thinning in the superior and middle temporal gyri in early PD patients compared with healthy controls [<xref rid="B39" ref-type="bibr">36</xref>, <xref rid="B64" ref-type="bibr">61</xref>]. Temporal lobe changes were shown to have a positive correlation with cognitive scores [<xref rid="B29" ref-type="bibr">29</xref>, <xref rid="B30" ref-type="bibr">30</xref>] and a negative correlation with the duration of disease in PD-MCI but not in PD-NC [<xref rid="B46" ref-type="bibr">43</xref>]. Longitudinal studies in PD-MCI patients reported a higher rate of cortical atrophy and an increased percentage of cortical thinning over time in the superior temporal gyrus and temporal pole in PD-MCI compared to PD-NC or healthy controls [<xref rid="B29" ref-type="bibr">29</xref>, <xref rid="B30" ref-type="bibr">30</xref>]. Additionally, deficits in neuropsychological memory tasks likely relying on the medium temporal lobe were reported to be more associated with incident dementia compared with the frontally based planning and working memory deficits [<xref rid="B13" ref-type="bibr">13</xref>]. Studies in non-PD populations reported that reduced medial and lateral temporal lobe atrophy was also present in MCI patients who converted to Alzheimer's disease [<xref rid="B14" ref-type="bibr">14</xref>, <xref rid="B65" ref-type="bibr">62</xref>]. Furthermore, temporal lobe atrophy was shown to be a powerful and independent predictor of conversion to dementia in relatively young MCI patients [<xref rid="B66" ref-type="bibr">63</xref>]. Overall, this would indicate that temporal lobe atrophy (specifically the parahippocampal gyrus, superior and middle temporal gyri) is strongly linked with cognitive impairment in both PD and non-PD populations (<xref ref-type="fig" rid="fig1">Figure 1</xref>, clusters 2, 3, and 8). This also highlights the importance of distinguishing between cognitive domains (e.g., amnestic versus nonamnestic) in PD-MCI in the early detection of patients who are likely to develop dementia rapidly.</p></sec><sec id="sec4"><title>4. Medial Occipital Cortex</title><p>A similar interpretation can be attributed to changes in the medial occipital lobe. Specifically the lingual region (<xref ref-type="fig" rid="fig1">Figure 1</xref>, cluster 9) was reported to have significant structural changes in PD-MCI patients compared with PD-NC. Gray matter loss in the lingual cortex has been reported by cross-sectional [<xref rid="B39" ref-type="bibr">36</xref>] and longitudinal studies [<xref rid="B29" ref-type="bibr">29</xref>]. This region also showed lower surface area, negative correlations with duration of disease [<xref rid="B46" ref-type="bibr">43</xref>], and cognitive scores over time [<xref rid="B30" ref-type="bibr">30</xref>]. In PDD patients bilateral occipital lobe atrophy was reported using VBM [<xref rid="B32" ref-type="bibr">32</xref>]. Metabolic changes were also described in the medial occipital lobe: increased hypoperfusion (compared with other brain regions) [<xref rid="B67" ref-type="bibr">64</xref>, <xref rid="B68" ref-type="bibr">65</xref>] and a greater cerebral glucose metabolic rate reduction (compared with healthy controls) [<xref rid="B69" ref-type="bibr">66</xref>]. Yet, another longitudinal study did not find significant cortical changes over time in PD-MCI patients in the occipital lobe [<xref rid="B30" ref-type="bibr">30</xref>]. From a functional point of view, the occipital structural changes in PD can be associated with the presence of hallucinations, since up to 40% of patients with PD have hallucinations and they are almost exclusively visual [<xref rid="B70" ref-type="bibr">67</xref>–<xref rid="B73" ref-type="bibr">70</xref>]. Previous studies reported a correlation between gray matter volume reduction and visual hallucinations in PD [<xref rid="B74" ref-type="bibr">71</xref>] as well as a dysfunction of the ventral visual pathway in PD patients with visual hallucinations and cognitive impairment [<xref rid="B75" ref-type="bibr">72</xref>]. Considering the above facts, it is still not clear whether occipital structural changes are linked with cognitive impairment or they reflect a more global cortical degeneration. Probably occipital lobe atrophy in patients who were diagnosed with PD can be considered as a marker of cognitive decline only if visual hallucinations are present.</p></sec><sec id="sec5"><title>5. Subcortical Structures</title><p>Anatomical neuroimaging cross-sectional studies in NDPD reported volume changes in subcortical gray matter structures in comparison to healthy controls. Cross-sectional and longitudinal studies reported lower volumes in the caudate nucleus, putamen, thalamus [<xref rid="B32" ref-type="bibr">32</xref>, <xref rid="B34" ref-type="bibr">34</xref>, <xref rid="B76" ref-type="bibr">73</xref>], hippocampus [<xref rid="B30" ref-type="bibr">30</xref>, <xref rid="B31" ref-type="bibr">31</xref>, <xref rid="B77" ref-type="bibr">74</xref>, <xref rid="B78" ref-type="bibr">75</xref>], and nucleus accumbens [<xref rid="B29" ref-type="bibr">29</xref>, <xref rid="B30" ref-type="bibr">30</xref>, <xref rid="B78" ref-type="bibr">75</xref>, <xref rid="B79" ref-type="bibr">76</xref>]. Further longitudinal analysis outlined that over time volume shrinkage in these structures is present only in PD-MCI patients and are absent in PD-NC [<xref rid="B29" ref-type="bibr">29</xref>], suggesting that volume parameters of subcortical structures in PD-NC are closer to the parameters of healthy controls, while volume losses are more specific for appearance of cognitive impairment. In support for this result, volume loss was reported in the nucleus accumbens only in PD-MCI patients [<xref rid="B29" ref-type="bibr">29</xref>, <xref rid="B30" ref-type="bibr">30</xref>], which was in line with the dopamine depletion theory in PD, according to which dopamine loss in PD progresses from the dorsal striatum (specifically caudate nucleus) to the ventral striatum (nucleus accumbens) and in early PD the dorsal striatum is severely depleted while the ventral striatum is relatively intact [<xref rid="B80" ref-type="bibr">77</xref>, <xref rid="B81" ref-type="bibr">78</xref>]. Hence, nucleus accumbens volume loss in PD-MCI patients can be considered as a marker for a higher level of dopamine depletion compared to PD-NC patients and a marker for the establishment of cognitive impairment.</p></sec><sec id="sec6"><title>6. Parietal Lobe</title><p>The structural changes in the parietal lobe have not been conclusive. The medial parietal cortex was reported to have decreased cortical thickness in one cross-sectional study [<xref rid="B63" ref-type="bibr">60</xref>]. The postcentral gyrus was reported with increased surface area cross-sectionally [<xref rid="B46" ref-type="bibr">43</xref>] and cortical gray matter shrinkage over time in PD-MCI patients compared with PD-NC or healthy controls [<xref rid="B30" ref-type="bibr">30</xref>]. A third cluster, right supramarginal (<xref ref-type="fig" rid="fig1">Figure 1</xref>, cluster 1), was shown to have a significant larger surface area in NDPD patients compared with healthy controls [<xref rid="B41" ref-type="bibr">38</xref>] and cortical shrinkage over time in PD-MCI compared to healthy controls [<xref rid="B30" ref-type="bibr">30</xref>]. Additionally, the right supramarginal and angular regions showed a positive correlation between cortical thickness and duration of disease in PD-MCI patients compared to PD-NC, thus showing a clear relation with cognitive decline in PD [<xref rid="B46" ref-type="bibr">43</xref>]. But most of these structural changes were not replicated by other studies. On the other hand, incident dementia was reported to be more specific for PD patients with deficits in neuropsychological tasks with a temporal and parietal lobe basis [<xref rid="B13" ref-type="bibr">13</xref>]. Thus, a marker in the parietal lobe could be considered reliable only if it was reported by several studies and in a big cohort. The supramarginal gyrus is one such cluster. Indeed, this region is involved in visual word recognition [<xref rid="B82" ref-type="bibr">79</xref>], intonation of speech [<xref rid="B83" ref-type="bibr">80</xref>], and the word's sounds [<xref rid="B84" ref-type="bibr">81</xref>] and in PD patients both speech production and self-monitoring of voiced speech are altered [<xref rid="B83" ref-type="bibr">80</xref>]. Nevertheless, future studies are needed in order to confirm the reliability of the supramarginal gyrus.</p></sec><sec id="sec7"><title>7. Conclusion</title><p>The diagnosis of MCI in PD should remain to be mainly clinical but neuroimaging techniques should be used as supportive findings. We suggest that several reliable MRI markers have already been distinguished and they can be used in order to predict cognitive decline in PD patients. Markers with an increased reliability that should be considered include the SMA and nucleus accumbens, along with atrophy in the temporal lobe (specifically parahippocampal, superior temporal, and middle temporal gyri) and medial occipital lobe (specifically the lingual area). Structural changes in other regions such as the DLPFC and VMPFC may also be markers of cognitive decline, but further studies are required to find out to what degree.</p><p>We suggest that current techniques could allow for the development of automated pipelines that measure the parameters of gray matter changes and that might be implementable on clinical MRI systems. Such approaches have the potential to be used in the future at the individual level after the creation of highly reliable average brains of PD patients with normal cognition and PD patients with MCI.</p></sec></body><back><ack><title>Acknowledgments</title><p>This work was funded by an operating grant from the Canadian Institutes of Health Research (MOP-126017) and the Tourmaline Oil Chair in Parkinson's Disease to Oury Monchi as well as a Parkinson Society Canada Basic Research Fellowship to Alexandru Hanganu.</p></ack><sec><title>Competing Interests</title><p>Authors declare that there are no competing interests regarding the publication of this paper.</p></sec><ref-list><ref id="B1"><label>1</label><element-citation publication-type="journal"><person-group person-group-type="author"><name><surname>De Rijk</surname><given-names>M. C.</given-names></name><name><surname>Tzourio</surname><given-names>C.</given-names></name><name><surname>Breteler</surname><given-names>M. M. 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Cluster nr 1: supramarginal gyrus; 2: superior temporal gyrus; 3: middle temporal gyrus; 4: dorsolateral prefrontal cortex (DLPFC); 5: ventromedial prefrontal cortex (VMPFC); 6: presupplementary motor area (pre-SMA); 7: supplementary motor area (SMA); 8: parahippocampal gyrus; 9: lingual area. Regions were drawn on the fsaverage subject in FreeSurfer 5.3 based on the Desikan atlas [<xref rid="B36" ref-type="bibr">82</xref>]. An inflated right hemisphere is presented (a) and the version outlining the gyri and sulci (b). SMA and pre-SMA were drawn based on the Brodmann atlas in FreeSurfer. The DLPFC region was drawn over Brodmann areas 9 and 46 [<xref rid="B37" ref-type="bibr">83</xref>] with the dorsal and ventral borders at the bottom of the superior and inferior frontal sulci, rostral border at the anterior termination of the olfactory sulcus [<xref rid="B38" ref-type="bibr">84</xref>], and the caudal border at the limit of Brodmann areas 8 and 9 [<xref rid="B37" ref-type="bibr">83</xref>]. Image created with GIMP Image Manipulation Program 2.8.16.</p></caption><graphic xlink:href="PD2016-3217960.001"></graphic></fig></floats-group></pmc></record>Pour manipuler ce document sous Unix (Dilib)
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