# BIOMARKERS TO DISENTANGLE THE PHYSIOLOGICAL FROM PATHOLOGICAL BRAIN AGING

EDITED BY : F. G. Guerini, W. S. Lim and B. Arosio PUBLISHED IN : Frontiers in Aging Neuroscience, Frontiers in Neurology and Frontiers in Neuroscience

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ISSN 1664-8714 ISBN 978-2-88963-769-0 DOI 10.3389/978-2-88963-769-0

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## BIOMARKERS TO DISENTANGLE THE PHYSIOLOGICAL FROM PATHOLOGICAL BRAIN AGING

Topic Editors: F. G. Guerini, Fondazione Don Carlo Gnocchi Onlus (IRCCS), Italy W. S. Lim, Tan Tock Seng Hospital, Singapore B. Arosio, University of Milan, Italy

Citation: Guerini, F. G., Lim, W. S., Arosio, B., eds. (2020). Biomarkers to Disentangle the Physiological From Pathological Brain Aging. Lausanne: Frontiers Media SA. doi: 10.3389/978-2-88963-769-0

# Table of Contents

*06 Editorial: Biomarkers to Disentangle the Physiological From Pathological Brain Aging*

Franca Rosa Guerini, Wee Shiong Lim and Beatrice Arosio

*09 Amyloid-*b *Load is Related to Worries, but Not to Severity of Cognitive Complaints in Individuals With Subjective Cognitive Decline: The SCIENCe Project*

Sander C. J. Verfaillie, Tessa Timmers, Rosalinde E. R. Slot, Chris W. J. van der Weijden, Linda M. P. Wesselman, Niels D. Prins, Sietske A. M. Sikkes, Maqsood Yaqub, Annemiek Dols, Adriaan A. Lammertsma, Philip Scheltens, Rik Ossenkoppele, Bart N. M. van Berckel and Wiesje M. van der Flier

*18 Moderating Effect of Cortical Thickness on BOLD Signal Variability Age-Related Changes*

Daiana R. Pur, Roy A. Eagleson, Anik de Ribaupierre, Nathalie Mella and Sandrine de Ribaupierre

*27 The Combination of DAT-SPECT, Structural and Diffusion MRI Predicts Clinical Progression in Parkinson's Disease*

Sara Lorio, Fabio Sambataro, Alessandro Bertolino, Bogdan Draganski and Juergen Dukart

## *40 Cognitive Profiles of Aging in Multiple Sclerosis*

Dejan Jakimovski, Bianca Weinstock-Guttman, Shumita Roy, Michael Jaworski III, Laura Hancock, Alissa Nizinski, Pavitra Srinivasan, Tom A. Fuchs, Kinga Szigeti, Robert Zivadinov and Ralph H. B. Benedict

*48 Effects of Brain Parcellation on the Characterization of Topological Deterioration in Alzheimer's Disease*

Zhanxiong Wu, Dong Xu, Thomas Potter, Yingchun Zhang and the Alzheimer's Disease Neuroimaging Initiative

Ryota Kobayashi, Hiroshi Hayashi, Shinobu Kawakatsu, Nobuyuki Okamura, Masanori Yoshioka and Koichi Otani

*87 Altered Static and Temporal Dynamic Amplitude of Low-Frequency Fluctuations in the Background Network During Working Memory States in Mild Cognitive Impairment*

Pengyun Wang, Rui Li, Bei Liu, Cheng Wang, Zirui Huang, Rui Dai, Bogeng Song, Xiao Yuan, Jing Yu and Juan Li

*97 On the Role of Adenosine A2A Receptor Gene Transcriptional Regulation in Parkinson's Disease*

Anastasia Falconi, Alessandra Bonito-Oliva, Martina Di Bartolomeo, Marcella Massimini, Francesco Fattapposta, Nicoletta Locuratolo, Enrico Dainese, Esterina Pascale, Gilberto Fisone and Claudio D'Addario

*107 HIV-Associated Neurocognitive Impairment in the Modern ART Era: Are We Close to Discovering Reliable Biomarkers in the Setting of Virological Suppression?*

Alessandra Bandera, Lucia Taramasso, Giorgio Bozzi, Antonio Muscatello, Jake A. Robinson, Tricia H. Burdo and Andrea Gori

Yingni Sun, Lisheng Liang, Meili Dong, Cong Li, Zhenzhen Liu and Hongwei Gao

*146 Brain Structural Correlates of Odor Identification in Mild Cognitive Impairment and Alzheimer's Disease Revealed by Magnetic Resonance Imaging and a Chinese Olfactory Identification Test*

Xingqi Wu, Zhi Geng, Shanshan Zhou, Tongjian Bai, Ling Wei, Gong-Jun Ji, Wanqiu Zhu, Yongqiang Yu, Yanghua Tian and Kai Wang

Marianna D'Anca, Chiara Fenoglio, Maria Serpente, Beatrice Arosio, Matteo Cesari, Elio Angelo Scarpini and Daniela Galimberti

Lih-Fen Lue, Ming-Chyi Pai, Ta-Fu Chen, Chaur-Jong Hu, Li-Kai Huang, Wei-Che Lin, Chau-Chung Wu, Jian-Shing Jeng, Kaj Blennow, Marwan N. Sabbagh, Sui-Hing Yan, Pei-Ning Wang, Shieh-Yueh Yang, Hiroyuki Hatsuta, Satoru Morimoto, Akitoshi Takeda, Yoshiaki Itoh, Jun Liu, Haiqun Xie and Ming-Jang Chiu

*191 Introducing a Novel Approach for Evaluation and Monitoring of Brain Health Across Life Span Using Direct Non-invasive Brain Network Electrophysiology*

Noa Zifman, Ofri Levy-Lamdan, Gil Suzin, Shai Efrati, David Tanne, Hilla Fogel and Iftach Dolev

*204 Longitudinal Assessment of Amyloid-*b *Deposition by [18F]-Flutemetamol PET Imaging Compared With [11C]-PIB Across the Spectrum of Alzheimer's Disease*

Shizuo Hatashita, Daichi Wakebe, Yuki Kikuchi and Atsushi Ichijo

*212 Elevations in Serum Dickkopf-1 and Disease Progression in Community-Dwelling Older Adults With Mild Cognitive Impairment and Mild-to-Moderate Alzheimer's Disease*

Laura Tay, Bernard Leung, Audrey Yeo, Mark Chan and Wee Shiong Lim

Cindy K. Barha, Chun-Liang Hsu, Lisanne ten Brinke and Teresa Liu-Ambrose

# Editorial: Biomarkers to Disentangle the Physiological From Pathological Brain Aging

Franca Rosa Guerini 1 \*, Wee Shiong Lim2 \* and Beatrice Arosio3,4 \*

*1 IRCCS Fondazione Don Carlo Gnocchi, Milan, Italy, 2 Department of Geriatric Medicine, Institute of Geriatrics and Active Ageing, Tan Tock Seng Hospital, Singapore, Singapore, 3 Geriatric Unit, Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, Milan, Italy, 4 Department of Clinical Sciences and Community Health, University of Milan, Milan, Italy*

Keywords: neurodegenerative diseases, aging, biomarkers, bioimage analysis, comorbidities

**Editorial on the Research Topic**

#### **Biomarkers to Disentangle the Physiological From Pathological Brain Aging**

The worldwide increase of human life expectancy ("lifespan") along with the concomitant rapid population aging represents the major social phenomena of the last century. This has substantially impacted our societies by virtue of the huge economic implications and public health challenges (Lim et al., 2018). In order to promote healthy aging and further increase the years of life spent without disabilities ("healthspan") (Beard et al., 2016; Olshansky, 2018), it is necessary to delve deep into the biology of aging and disentangle the mechanisms that underpin the physiological processes from those leading to pathological manifestation.

In this context, dementia as well as others common neurodegenerative diseases are strongly correlated with age. Although advancing age represents the major risk factor for cognitive decline, dementia is not an inevitable consequence of long life, as clearly demonstrated by centenarians who managed to preserve normal cognitive performance despite their age (Arosio et al., 2017). Against this backdrop, the ongoing "biomarker revolution" has been instrumental in transforming the landscape of medical research and practice. The relevance of biomarkers to the field of physiological and pathological brain aging is underscored by two considerations. Firstly, the relative lack of accessibility of brain issue for diagnostic or research purposes, and secondly, the understanding that neurodegenerative diseases are often characterized by a long preclinical phase has fuelled transition toward a biology-grounded framework and definition such as the NIA-AA Research Framework for Alzheimer's disease (AD) (Jack et al., 2018).

However, the available biomarkers currently used for AD generally describe single aspects of the patient's state and are only modestly associated with clinically meaningful manifestations. The combination of biomarkers capable of assessing the "real" biological age of the individual may thus represent a viable strategy to support the identification of disease-specific trajectories and, in parallel, provide insights about the aging phenomenon. The present Research Topic is therefore timely in advancing the ongoing discourse beyond the need of disentangling "physiological" from "pathological" brain aging to deepening existing understanding and offering new vistas for further development in the field.

Using cerebrospinal fluid (CSF) biomarkers for AD as a paradigmatic example, Canevelli et al. discussed the methodological issues and challenges that pertain to four key areas: (1) definition of reference values; (2) identification of reference standards specific for the disease of interest (i.e., AD); (3) proper inclusion and contextualization within the diagnostic process; and (4) statistical processes supporting the whole framework. They concluded that various methodological issues

Edited and reviewed by:

*Thomas Wisniewski, New York University, United States*

#### \*Correspondence:

*Franca Rosa Guerini<EMAIL_ADDRESS>Wee Shiong Lim<EMAIL_ADDRESS>Beatrice Arosio<EMAIL_ADDRESS>
Received: *27 February 2020* Accepted: *16 March 2020* Published: *15 April 2020*

#### Citation:

*Guerini FR, Lim WS and Arosio B (2020) Editorial: Biomarkers to Disentangle the Physiological From Pathological Brain Aging. Front. Aging Neurosci. 12:88. doi: 10.3389/fnagi.2020.00088* remain to be addressed in order to perform an adequate and complete clinical validation of candidate CSF biomarkers for AD.

Five papers in this Research Topic shed precious insights into emerging biomarkers from readily available biological samples. In their comprehensive review, D'Anca et al. explored the current understanding of role of exosomes in physiological aging and age-related neurodegenerative diseases such as AD, Parkinson's Disease (PD) and frontotemporal dementia. Insights from the study of exosomes and their genetic cargo (such as lipid, proteins, mRNAs, and ncRNAs) have elevated their role beyond mere waste disposal function to fundamental mediators of intercellular communication, akin to the proverbial doubleedge sword serving as Trojan horses of neurodegeneration vis-à-vis providing neuroprotection from neurodegeneration. Exosomes can be detected in many biological fluids, enhancing their appeal as potential sources of biomarkers suitable for use in clinical practice.

Similarly, Tay et al. prospectively studied serum levels of Dickkopf-1 (Dkk-1) in older adults with mild cognitive impairment (MCI) and mild-to-moderate AD. The findings revealed that Dkk-1 increased significantly from baseline amongst progressors, while non-progressors exhibited decremental Dkk-1 at 1 year, alluding to the putative role in MCI and AD progression of dysfunctional Wnt signaling through Dkk-1 antagonism. Sun et al. demonstrated that cofilin 2 expression was significantly increased in AD patients and different AD models (animal and cell), with good discriminatory ability that distinguish AD from healthy subjects and in differential diagnosis of AD from vascular dementia. By studying 391 cognitively normal subjects aged 23–91 years from Asia, USA and Europe, Lue et al. characterized the relationship between age and three plasma AD core biomarkers (Aß40, Aß42, and t-Tau), provided the normal ranges of Aß species and t-Tau in plasma, and explicated the development of a dynamic relationship between these biomarkers from middle to old age. Lastly, Falconi et al. investigated the transcriptional regulation of the Adenosine A2A receptors (A2ARs) gene in human peripheral blood mononuclear cells obtained from PD patients and in the striatum of the 6-hydroxydopamine-induced PD mouse model. They reported an increase in A2AR mRNA expression and protein levels in both human cells and mice that is accompanied by histone acetylation and DNA methylation, paving the way for therapeutic interventions in future.

Another prominent theme in this Research Topic was the contribution of advanced neuroimaging biomarkers. For instance, in terms of explicating mechanisms that underpin underlying pathophysiological processes, Wang, Li et al. shed light on the neural mechanisms of working memory deficits in MCI. By combining static and temporal dynamic examination of amplitude of low-frequency fluctuations (ALFF) from functional magnetic resonance imaging, they reported background network changes especially in the parietal and temporal lobes during working memory states in MCI. Similarly, Pur et al. enhanced understanding by demonstrating the moderating effect of cortical thickness on blood oxygen leveldependent (BOLD) signal variability age-related changes and highlight the importance of considering these effects when evaluating BOLDSD alternations across the lifespan. Kobayashi et al. examined whether dementia with Lewy Bodies (DLB) follows an AD-type trajectory whereby amyloid-ß deposition begins considerably before onset of dementia. They observed a low amyloid load in REM sleep behavioral disorder, a prodromal symptom of DLB, suggesting that this phenomenon does not always precede the onset of cognitive decline in DLB. Lastly, through the use of MRI voxel studies to examine neural substrates, Wu, Geng et al. implicated the left-precentral cortex and left inferior frontal gyrus areas that accounted for olfactory impairment in a cohort of AD and MCI patients in the Chinese Han population.

Other papers in this Research Topic illuminated the knowledge gap in our understanding about the role of misfolded protein accumulation in neurodegenerative diseases. The extracellular accumulation of amyloid beta (Aβ) peptide is the paradigmatic example, with recent neuroimaging capabilities in tracking Aβ deposition a fascinating way to distinguish if the observed Aβ accumulation is characteristic of the aging process or conversely, an etio-pathogenetic mechanism of AD (Canevelli et al., 2017). Hatashita et al. examined [18F] flutemetamol (FMM), a fluorinated derivative of the prototypic [11C]-Pittsburgh Compound B (PIB), and demonstrated that [18F]-FMM PET imaging can track longitudinal changes in Aβ deposition across the AD spectrum, similarly to [11C]-PIB PET. Notably, they reported that the increase in Aβ deposition is not constant across the AD spectrum but faster in the predementia stage (Hatashita et al.). Using dynamic [18F] florbetapir PET, Verfaillie et al. investigated the possibility that self-perceived cognitive decline (SCD), generally associated with a three- to six-fold increased risk of AD, may reflect an early symptom of Aβ related pathology. They concluded that Aβ load was associated with SCD related worries rather than subjective cognitive functioning per se (Verfaillie et al.).

Other papers highlight advances in neuroimaging techniques that pave the way for fresh perspectives in early diagnosis of AD. Utilizing sophisticated measurements of cortical thickness with 3.0-Tesla MRI in a large population of cognitively normal individuals and patients with AD continuum, Lee et al. described an age-dependent cortical thinning with relative sparing of the precuneus and inferior temporal regions. In contrast, late-stage amnestic MCI and moderate to severe AD were associated with widespread cortical thinning including the precuneus and inferior temporal regions (Lee et al.). Furthermore, the study by Wu, Xu et al. provides proof-of-concept evidence that a topological examination of the structural connectivity networks with different parcellation schemes can provide important complementary AD-related information and thus contribute to a more accurate and earlier diagnosis of AD. Another promising technique involved amide proton transfer (APT) imaging as an imaging modality to detect tissue protein. In fact, Wang, Chen et al. adopting a modified APT method (APTSAFARI on a 7.0T animal MRI scanner) in animal models, demonstrated that APT imaging could potentially provide molecular biomarkers for non-invasive diagnosis of AD. Using direct non-invasive brain network electrophysiological imaging, Zifman et al. established that this new technique can be used both to monitor brain aging and for early detection of abnormal changes leading to neurodegeneration.

Bioimaging techniques are also useful for the differential diagnosis of PD. Dopamine-transporter SPECT (DAT-SPECT), diffusion tensor imaging (DTI), and structural magnetic resonance imaging (sMRI) provide unique information about neurotransmitters and microstructural properties in PD. The longitudinal study of Lorio et al. described how the combination of these imaging modalities can be used as biomarkers of PD severity and prognosis which can be potentially useful for clinical trials. Likewise, Pelizzari et al. proposed the concomitant use of DTI to detect brain tissue microstructural alterations, together with arterial spin labeling (ASL) MRI to analyse abnormal cerebral perfusion patterns in PD. The study showed that DTI is a more sensitive technique than ASL to detect alterations in the basal ganglia in the early phase of PD, suggesting that a relationship between microstructural integrity and perfusion changes in the caudate may be present (Pelizzari et al.).

Papers in this Research Topic also addressed the issue of ageassociated co-morbidities, particularly cardiovascular diseases, and related risk factors such as Type II diabetes mellitus (T2DM), which often confound the measurement and interpretation of neurodegenerative biomarkers. Exploring the association between white matter hyperintensities with higher Intima-media thickness (IMT) and blood pressure variability, Chen et al. developed an innovative predictive model to evaluate white matter burden in hypertensive patients using metrics in 24-h ambulatory BP monitoring (systolic blood pressure [SBP] and daytime SBP standard deviation) and carotid ultrasound IMT. Leveraging upon the diagnostic potential of diabetic retinopathy (DR) afforded by its time-course which precedes the occurrence of T2DM cognitive impairment, Lu et al. verified the correlation between DR from fundus examination and T2DM cognitive impairment. They further established using magnetic resonance spectroscopy (1H-MRS) that this may be attributed to bilateral changes in hippocampal brain metabolism, alluding to the potential role of 1H-MRS for early diagnosis of T2DM cognitive impairment (Lu et al.).

A major pathogenetic influence of co-morbidities or their treatment that have emerged in recent years is that of concomitant inflammatory processes that may alter disease manifestation and confound biomarker interpretation. An example is the cognitive decline observed in some HIV

### REFERENCES

subjects during ART-mediated viral suppression, a phenomena which has been ascribed to cytokine-mediated inflammation. Bandera et al. suggested an omics approach (transcriptomics, proteomics, and metabolics) to accelerate the discovery of reliable biomarkers of HIV-associated neurocognitive disorders in the current era of virological suppression with modern anti-retroviral therapy. Similarly, persistent inflammation has been implicated as a cardinal mechanism of neurocognitive impairment and neurodegenerative features in Multiple Sclerosis (MS). Jakimovski et al. reported the novel finding that almost half the elderly subjects with MS are impaired on tests of cognitive processing speed or verbal fluency. Since the deficit in verbal fluency is not a typical hallmark of the cognitive profile associated with MS, it may represent a unique trait of old persons with MS neurocognitive profile (Jakimovski et al.).

Lastly, there is growing interest in the role of physical activity and exercise as "medicine" that can be prescribed for brain health. In their comprehensive review, Barha et al. underlined the role of biological sex as a potential moderator in the relationship between physical activity and brain health in older adults. They suggested that different neurobiological mechanisms (e.g., neurotrophic factors, neuroplastic processes, hormones, and neurotransmitter systems) as well as physiological adaptations to physical activities could be responsible for the differences in trajectories of decline observed in men and women (Barha et al.). This highlights the importance of taking into consideration the potent moderating influence of sex differences for both cognitive and neural outcomes in future therapeutic and rehabilitative interventions involving physical activity in older adults.

As guest editors for this Research Topic, we commend this collection of 23 articles to our readers as a timely addition and important contribution to the field. We are confident that this will spur further discourse and open avenues for further research into the rapidly evolving and fascinating area of biomarkers to disentangle physiological from pathological brain aging.

### AUTHOR CONTRIBUTIONS

FG, WL, and BA conceived the manuscript. FG and WL drafted the paper. BA critically appraised and edited the manuscript. All authors read and approved the final version of the paper.

**Conflict of Interest:** The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Copyright © 2020 Guerini, Lim and Arosio. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

# Amyloid-β Load Is Related to Worries, but Not to Severity of Cognitive Complaints in Individuals With Subjective Cognitive Decline: The SCIENCe Project

Sander C. J. Verfaillie1,2,3 \*, Tessa Timmers1,2,3, Rosalinde E. R. Slot1,3 , Chris W. J. van der Weijden2,3, Linda M. P. Wesselman1,3, Niels D. Prins1,3 , Sietske A. M. Sikkes1,3,4, Maqsood Yaqub2,3, Annemiek Dols1,3,5 , Adriaan A. Lammertsma2,3, Philip Scheltens1,3, Rik Ossenkoppele1,3,6 , Bart N. M. van Berckel2,3 and Wiesje M. van der Flier1,3,4

1 Department of Neurology and Alzheimer Center, Amsterdam University Medical Center, Vrije Universiteit Amsterdam, Amsterdam, Netherlands, 2 Department of Radiology and Nuclear Medicine, Amsterdam Neuroscience, Amsterdam University Medical Center, Vrije Universiteit Amsterdam, Amsterdam, Netherlands, 3 Amsterdam Neuroscience, Amsterdam, Netherlands, 4 Department of Epidemiology and Biostatistics, Amsterdam Neuroscience, Amsterdam University Medical Center, Vrije Universiteit Amsterdam, Amsterdam, Netherlands, 5 Department of Old Age Psychiatry, Amsterdam Neuroscience, GGZ inGeest, Amsterdam University Medical Center, Vrije Universiteit Amsterdam, Amsterdam, Netherlands, 6 Clinical Memory Research Unit, Lund University, Malmö, Sweden

### Edited by:

Beatrice Arosio, University of Milan, Italy

#### Reviewed by:

Gabriel Gonzalez-Escamilla, Johannes Gutenberg University Mainz, Germany Martina Casati, IRCCS Ca' Granda Foundation Maggiore Policlinico Hospital, Italy

\*Correspondence:

Sander C. J. Verfaillie<EMAIL_ADDRESS>
Received: 02 November 2018 Accepted: 10 January 2019 Published: 25 January 2019

#### Citation:

Verfaillie SCJ, Timmers T, Slot RER, van der Weijden CWJ, Wesselman LMP, Prins ND, Sikkes SAM, Yaqub M, Dols A, Lammertsma AA, Scheltens P, Ossenkoppele R, van Berckel BNM and van der Flier WM (2019) Amyloid-β Load Is Related to Worries, but Not to Severity of Cognitive Complaints in Individuals With Subjective Cognitive Decline: The SCIENCe Project. Front. Aging Neurosci. 11:7. doi: 10.3389/fnagi.2019.00007 Objective: Subjective cognitive decline (SCD) is associated with an increased risk of Alzheimer's Disease (AD). Early disease processes, such as amyloid-β aggregation measured with quantitative PET, may help to explain the phenotype of SCD. The aim of this study was to investigate whether quantitative amyloid-β load is associated with both self- and informant-reported cognitive complaints and memory deficit awareness in individuals with SCD.

Methods: We included 106 SCD patients (mean ± SD age: 64 ± 8, 45%F) with 90 min dynamic [18F]florbetapir PET scans. We used the following questionnaires to assess SCD severity: cognitive change index (CCI, self and informant reports; 2 × 20 items), subjective cognitive functioning (SCF, four items), and five questions "Do you have complaints?" (yes/no) for memory, attention, organization and language), and "Does this worry you? (yes/no)." The Rivermead Behavioral Memory Test (RBMT)- Stories (immediate and delayed recall) was used to assess objective episodic memory. To investigate the level of self-awareness, we calculated a memory deficit awareness index (Z-transformed (inverted self-reported CCI minus episodic memory); higher index, heightened self-awareness) and a self-proxy index (Z-transformed self- minus informantreported CCI). Mean cortical [18F]florbetapir binding potential (BPND) was derived from the PET data. Logistic and linear regression analyses, adjusted for age, sex, education, and depressive symptoms, were used to investigate associations between BPND and measures of SCD.

Results: Higher mean cortical [18F]florbetapir BPND was associated with SCDrelated worries (odds ratio = 1.76 [95%CI = 1.07 ± 2.90]), but not with other SCD

**9**

questionnaires (informant and self-report CCI or SCF, total scores or individual items, all p > 0.05). In addition, higher mean cortical [18F]florbetapir BPND was associated with a higher memory deficit awareness index (Beta = 0.55), with an interaction between BPND and education (p = 0.002). There were no associations between [18F]florbetapir BPND and self-proxy index (Beta = 0.11).

Conclusion: Amyloid-β deposition was associated with SCD-related worries and heightened memory deficit awareness (i.e., hypernosognosia), but not with severity of cognitive complaints. Our findings indicate that worries about self-perceived decline may reflect an early symptom of amyloid-β related pathology rather than subjective cognitive functioning.

Keywords: subjective cognitive decline (SCD), Alzheimer's diseaese, amyloid PET, self-awareness, early – biomarkers

### INTRODUCTION

Amyloid-β plaques and neurofibrillary tangles are neuropathological hallmarks of Alzheimer's disease (AD), which start to appear 10–20 years before the onset of dementia (Jack et al., 2013). Self-perceived cognitive decline in cognitively normal individuals is associated with a three- to six fold increased risk of AD (Schmand et al., 1996; Geerlings et al., 1999; Jessen et al., 2010). As such, a proportion of individuals with subjective cognitive decline (SCD) may harbor the earliest pathological changes associated with AD (Verfaillie et al., 2016, 2018a,b), particularly amyloid-β accumulation (i.e., preclinical AD) (Buckley et al., 2016; Perrotin et al., 2016; Hu et al., 2018).

Only a minority of individuals with SCD will develop AD within a few years (Slot et al., 2018a), but it is conceivable that individuals with preclinical AD exhibit a specific phenotype of cognitive complaints compared with individuals without underlying AD. There are many questionnaires to investigate the nature and severity of SCD, but the appropriate items enabling prediction of conversion to mild cognitive impairment (MCI) and dementia have not yet been identified (Rabin et al., 2015, 2017). There are various methodological challenges associated with SCD assessment, one of which is that cognitive complaints tend to vary as a function of demographic characteristics, such as level of education and age (Rabin et al., 2015). In addition, these factors can also act in synergy; for example, it has been shown that cognitive complaints in highly educated individuals are associated with increased risk of progression to AD, while this is not found in individuals with lower education levels (Jonker et al., 2000; van Oijen et al., 2007). SCD plus criteria were proposed in an effort to increase the likelihood of identifying preclinical AD in individuals with SCD. One of these criteria suggests that especially individuals who worry about their selfperceived cognitive decline are more likely to have preclinical AD, but associations between worries and amyloid-β load have not been confirmed in prospective studies yet (Geerlings et al., 1999; Jessen et al., 2010, 2014). Furthermore, former studies investigating associations between amyloid-β load and various SCD questionnaires have generated highly inconsistent results in cognitively normal individuals (Rodda et al., 2010; Amariglio et al., 2012; Mielke et al., 2012; Perrotin et al., 2012, 2016; Buckley et al., 2013; Holland et al., 2015; Snitz et al., 2015). These discrepancies could be due to the less precise amyloid-β positron emission tomography (PET) semi-quantitative cut-off values in preclinical stages of AD (Villeneuve et al., 2015), and variability of implemented SCD questionnaires, including the lack of informant reports or objective memory tests relative to self-reports (Rabin et al., 2015, 2017).

Another approach is to explore whether preclinical AD is linked to the insight in cognitive deficits (i.e., self-awareness) rather than to the severity of SCD. The degree of memory deficit awareness takes into account the contribution of objective memory performance or informant reports relative to self-reports of cognitive decline (Barba et al., 1995; Perrotin et al., 2015). A lack of awareness of memory deficits, anosognosia, is a striking symptom in patients with AD dementia (Barba et al., 1995; Perrotin et al., 2015). On the contrary, it is has been suggested that the earliest changes in cognition during preclinical stages of the disease are best perceived by the individual including a heightened sense of self-awareness for early brain changes (i.e., hypernosognosia) (Caselli et al., 2014; Langer and Levine, 2014). In a recent study self-awareness was defined as a discrepancy score between subjective and objective episodic memory performance and they found that cognitively normal individuals harboring amyloid-β pathology had a heightened sense of self-awareness (Vannini et al., 2017). It has, however, not yet been investigated whether the earliest changes in cognition are best perceived by the individual rather than the observer or objective memory tests in a memory clinic setting.

We hypothesized that increased amyloid-β load is related to specific cognitive complaints and heightened level of selfawareness. Therefore, the purpose of the present study was to investigate whether amyloid-β load, as measured using quantitative PET, may help to explain the phenotype of SCD in cognitively normal individuals who initially have been referred to a memory clinic. A second aim was to investigate whether amyloid-β load is associated with altered levels of self-awareness and informant reports of cognitive change.

## MATERIALS AND METHODS

fnagi-11-00007 January 24, 2019 Time: 16:44 # 3

We included 106 SCD memory-clinic patients with [ 18F]florbetapir PET scans from the ongoing Subjective Cognitive ImpairmENt Cohort (SCIENCe) study (Slot et al., 2018b). Subjects were referred to our memory clinic by their general practitioner or medical specialist because of cognitive complaints. Prior to inclusion via the memory clinic, all patients underwent a standardized dementia screening according to the procedures of the Amsterdam Dementia Cohort (Van Der Flier et al., 2014). Screening included extensive neuropsychological assessment, physical and neurologic examination as well as laboratory serum tests (hemoglobin, thrombocytes, leucocytes, TSH, MCH, MCV, erycytes), and brain magnetic resonance imaging (MRI). A Dutch translation of the mini-mental state examination (MMSE) was used to screen for global cognition (Folstein et al., 1975). Clinical diagnosis was established by consensus in a multidisciplinary team. Individuals were labeled as having SCD when they presented with cognitive complaints, and results of clinical investigations were within normal range. Criteria for MCI, dementia, or any other neurological or psychiatric (e.g., major depression) disorders known to cause cognitive complaints were not met (Jessen et al., 2014; Molinuevo et al., 2017). In addition, we used the Hospital Anxiety and Depression Scale- Anxiety subscale (HADS-A) and Center for Epidemiological Studies Depression Scale (CES-D) scale to evaluate (subclinical) anxiety and depressive symptoms (cut-off ≥ 16), respectively (Radloff, 1977; Zigmond and Snaith, 1983). The study had been approved by the Medical Ethics Review Committee of the VU University Medical Center. All patients provided written informed consent.

### Image Acquisition and Analyses

Ninety minutes dynamic [18F]florbetapir PET scans were acquired on a PET/CT scanner (n = 59 on an Ingenuity TF and n = 47 on a Gemini TF, both from Philips Medical Systems, Best, Netherlands). PET images were corrected for attenuation, scatter, randoms, decay and dead time using standard software provided by Philips Healthcare. Three-dimensional T1-weighted MRI scans were co-registered to the PET scans, and regions of interest (Hammers template, n = 68 regions of interest [ROI]) were defined on the MRI scan (in native space) and superimposed onto the dynamic PET scan to obtain regional time activity curves using PVElab (Hammers et al., 2003; Rask et al., 2004). Receptor parametric mapping (RPM) with optimized settings (parameters settings 0.01–0.1, 50 basis functions) and cerebellar gray matter as reference region was used to generate images of binding potential (BPND) relative to the non-displaceable compartment (Lammertsma and Hume, 1996; Gunn et al., 1997; Golla et al., 2018). From the BPND images, gray matter volume-weighted mean cortical BPND values were obtained. To investigate potential regional specificity, volume-weighted bilateral frontal, temporal (medial and lateral), and parietal cortical BPND values were also extracted. In addition, standardized uptake value (SUV, 50–70 min post-injection) images were visually assessed by a trained and experienced reader (BvB), leading to "normal" or "abnormal" classification of amyloid accumulation (for more details regarding visual reading of [18F]florbetapir PET images, please see https://www.accessdata.fda.gov/drugsatfda\_ docs/label/2012/202008s000lbl.pdf).

### SCD Assessment

We used four questionnaires with the following characteristics: two self-, one informant-based questionnaires, and one which was composed of five cognitive questions to assess SCD. The maximal time window between these assessments and the PET scan was 1 year (median = 3 months). We used both self- and informant reports of the Dutch translation of the Cognitive Change Index (CCI self and informant versions; each 20 questions [range 0–4], total score: 20–100) to assess cognitive function compared to 5 years ago (Rattanabannakit et al., 2016). We used the Subjective Cognitive Functioning (SCF, self-report) questionnaire (4 questions, range: –12 to +12) to assess selfexperienced cognitive decline over a one-year time period.(Van Der Flier et al., 2014) SCF scores were inverted in such a way that higher scores reflect more complaints, comparable to the CCI. Finally, we used a structured patient interview to assess SCD. We used the following question "What complaints do you report?". Based on the individuals' spontaneous response the following cognitive domains were scored "yes/no": memory, attention, organization, language, together with the follow-up question: "Does this worry you?" (Geerlings et al., 1999; Jessen et al., 2010). In addition, for descriptive purposes, the following question was used to inquire SCD onset "when was the first time that you talked with a physician about these problems?"

### Memory Self-Awareness Indexes

To investigate the level of self-awareness, two index scores were calculated. First, the memory deficit awareness index, was defined for each participant by calculating the difference between subjective and objective episodic memory scores (Barba et al., 1995; Perrotin et al., 2015; Vannini et al., 2017). In concordance with previous studies we used episodic memory (%remembered = [immediate/delayed recall] ∗ 100%) for the memory deficit awareness index, i.e., the Rivermead Behavioral Memory Test (RBMT)-Stories. To allow comparison between both measures, (1) the CCI-self was inverted in such a way that, similar to the objective memory score, a lower score indicated more severe subjective memory impairment; (2) both objective and the subjective memory scores were Z-transformed (Barba et al., 1995; Perrotin et al., 2015; Vannini et al., 2017). A positive index score reflects heightened selfawareness (hypernosognosia), whereas negative scores lowered self-awareness (anosognosia) (Barba et al., 1995; Perrotin et al., 2015; Vannini et al., 2017). To test the robustness of the memory deficit awareness index, we repeated the aforementioned procedures while using the Dutch version of the Rey auditory verbal learning test (RAVLT; immediate [5 trials summed] and delayed recall). Second, a self-proxy index (self-reported CCI minus informant-reported CCI) was calculated.

### Statistical Analyses

fnagi-11-00007 January 24, 2019 Time: 16:44 # 4

Statistical analyses were performed using Statistical Package for the Social Sciences (SPSS, IBM v22). We used linear regression (for continuous outcome measures) or binary logistic regression (for dichotomous outcome measures) analyses to investigate associations between mean cortical [18F]florbetapir BPND (independent variable) and measures of SCD (i.e., CCI, SCF [total and single items scores], complaints questions). Analyses were adjusted for age, sex, education and depressive symptoms (CES-D). As cognitive complaints in highly educated individuals may be more predictive of dementia (Jonker et al., 2000; van Oijen et al., 2007), we also tested for an interaction education∗ [ 18F]florbetapir BPND. We repeated analyses for cortical lobar [18F]florbetapir BPND. Linear regression analyses, adjusted for age, sex, education and depressive symptoms, were used to investigate associations between [18F]florbetapir BPND (independent variable) and memory self-awareness indexes (dependent variables; separate models). In addition, we also tested for an interaction education∗ [ 18F]florbetapir BPND. Associations were considered significant if p < 0.05.

### RESULTS

Demographic and clinical data are presented in **Table 1**. Individuals (43% females) were (mean ± SD) 64 ± 8 years old and had an MMSE of 29 ± 1. Twenty-four individuals (23%) showed abnormal amyloid-β accumulation. On average, subjects had a mean cortical BPND of 0.18 ± 0.15 (**Figure 1**; frontal cortex 0.18 ± 0.18, temporal cortex 0.13 ± 0.13, cingulate cortex 0.25 ± 0.19, parietal cortex 0.22 ± 0.17). On average, individuals reported lower subjective cognitive functioning than 1 year earlier (SCF = –1.54 ± 2.92), and slight to occasional problems (CCI self-report: 41.23 ± 15.05; CCI informant report: 37.17 ± 16.44) compared to 5 years ago. There were no differences between self- and informant-based reports regarding the degree of cognitive change over a 5 year period (i.e., both CCI versions). About 68% (n = 73), 34% (n = 36), 13% (n = 14) and 25% (n = 27) of the individuals reported complaints in the domains of memory, language, organization and attention, respectively, whilst 47% (n = 50) felt worried about their selfperceived decline.

Associations between mean cortical [18F]florbetapir BPND and measures of SCD are presented in **Table 2**. Adjusted for age, sex, education and depressive symptoms (CES-D), higher mean cortical (**Figure 2**) [18F]florbetapir BPND was associated with two-fold increased risk of SCD-related worries, but neither with any item nor total score of the SCF nor CCI (neither informant-based nor self-reported) nor dichotomous memory (**Figure 2**), attention, organization or language questions (all p > 0.05). There were no interaction effects between mean cortical [18F]florbetapir BPND and education for any of the SCD questionnaire outcomes (all p > 0.05). If we repeated analyses with cortical lobar [18F]florbetapir BPND, we found that frontal TABLE 1 | Clinical and demographic data.

Data are presented as means (standard deviations) or percentages. Education level was assessed using the Verhage classification in accordance with the Dutch educational system. SCD onset was based upon individuals' self-reports. Depressive symptoms were assessed with the CES-D.

corresponding 95% confidence intervals.

Data are presented as standardized Beta (unstandardized beta +standard error) or ∗odd ratios (95% confidence intervals). Analyses were adjusted for age, sex, education and depressive symptoms. Analyses between amyloid-β load, selfproxy and self-awareness indexes were based on mean cortical amyloid-β load. Self-awareness indexes; positive associations (i.e., betas) reflect heightened selfawareness (hypernosognosia) in relation with higher amyloid-β load, whereas negative scores reflect lowered self-awareness (anosognosia).

(Odds ratio (OR) [95% confidence interval] = 1.70 [1.05–2.76]), cingulate (OR = 1.70 [1.06–2.73]), parietal (OR = 1.72 [1.08– 2.74]), and temporal (OR = 1.86 [1.10–3.14]) cortical regions were associated with SCD-related worries, but not with other SCD questionnaires (all p > 0.05). Results remained essentially comparable when we repeated analyses while additionally adjusting for PET/CT scanner systems (data not shown).

We furthermore investigated associations between mean cortical [18F]florbetapir BPND and two self-awareness indexes. We found that higher mean cortical [18F]florbetapir BPND was associated with a higher memory deficit awareness index (i.e., hypernosognosia) (**Table 2** and **Figure 2**), with an interaction between BPND and education implying that this effect was stronger for individuals with relatively lower education (please see the full statistical models in **Supplementary Table S1**). When we repeated analyses with the memory deficit awareness index based on the RAVLT we found comparable results (**Table 2** and **Figure 2E** right panel). There were no associations between mean cortical BPND and the self-proxy index, indicating that amyloid load was not related to discrepancy scores between self- and informant reports (based on the CCI).

### DISCUSSION

The main finding of the present study is that amyloid-β load is associated with an approximately two-fold increased risk of SCDrelated worries and a heightened memory-deficit self-awareness, but not with severity or specific cognitive complaints.

Amyloid-β load may insidiously affect cognition and selfperceived decline prior to symptom onset (Perrotin et al., 2012; Snitz et al., 2015; Baker et al., 2017), which could be amongst others a reason for individuals to visit a memory clinic. Some population-based and mixed population and memory clinic studies have shown that amyloid-β load is related to SCD (Amariglio et al., 2012; Mielke et al., 2012; Perrotin et al., 2012, 2016; Snitz et al., 2015), but other studies did not find this association with SCD (Buckley et al., 2013; Holland et al., 2015). To the best of our knowledge, associations between amyloidβ load and cognitive complaints have not been investigated in a pure memory clinic sample, and earlier findings could have been affected by the recruitment policy, particularly in the case of mixed recruitment studies (Jansen et al., 2015; Slot et al., 2018a). Therefore, it remains unclear to what extent amyloidβ is contributing to the phenotype of SCD in individuals who seek medical evaluation for their complaints (Jonker et al., 2000; Stewart, 2012). In the present study we investigated relatively young individuals with a recent SCD onset (<5 years), and compared to literature (Jansen et al., 2015; Ossenkoppele et al., 2015), a substantial fraction of almost one out of four showed abnormal amyloid-β accumulation. We furthermore used quantitative PET (i.e., BPND) because it can more accurately determine amyloid-β load than standardized uptake ratio values (SUVr), which is especially important to capture potentially early – subtle – disease processes, and SCD-related worries are related to a sixteen and six fold increased risk of clinical progression, respectively (Jessen et al., 2010; Van Harten et al., 2013; Buckley et al., 2016), and we now show that they associated with each other in cognitively normal individuals. The SCD plus criteria have suggested that the presence of SCD-related worries are associated with an increased risk of future cognitive decline (Jessen et al., 2014), and our results support this notion.

Apart from the relationship with SCD-related worries, no associations between amyloid-β load and any of the SCD questionnaires or single items that measure various aspects of cognitive change were observed, indicating that these questionnaires are not specific for amyloid-β accumulation in a memory clinic. There is much controversy about which questionnaire can be used to unveil cognitive normal individuals at increased risk for AD, but the present results indicate that it does not necessarily matters which questionnaire, but rather whether the SCD assessment includes a "worry" inquiry. Earlier studies have used SUVr to assess abnormal amyloid-β accumulation, which have generated inconsistent results (Mielke et al., 2012; Perrotin et al., 2012, 2016; Holland et al., 2015; Snitz et al., 2015). Compared with BPND, SUVr is liable to overestimation of amyloid-β load together with a higher variability (van Berckel et al., 2013), which could hamper a correct interpretation and reduce statistical power especially in early disease stages (Yaqub et al., 2008; van Berckel et al., 2013; Golla et al., 2018). Other possible explanations for our findings are recruitment criteria used and the operationalization of SCD (Perrotin et al., 2016; Molinuevo et al., 2017). A recent study has demonstrated elevated levels of amyloid-β load in memory clinic SCD patients compared with communitydwelling individuals without SCD, but not compared with community-recruited subjects with SCD (Perrotin et al., 2016). The present investigations were restricted to individuals with SCD, who had visited a memory clinic for their self-perceived

FIGURE 2 | (A) Mean cortical amyloid-beta load in relation to raw (untransformed) scores of the self-report cognitive change index (CCI) and (B) informant-based CCI, and (D) the subjective cognitive functioning (SCF) questionnaire. (C) Mean cortical amyloid-beta load stratified for memory complaints (yes/no) and worries (yes/no). (E) Associations between mean cortical amyloid load and self-awareness index based on the RBMT % delayed recall (left) and RAVLT % delayed recall (right) with stratification for low and high education level. Memory deficit awareness index: A positive index score reflects heightened self-awareness (hypernosognosia), whereas negative scores lowered self-awareness (anosognosia).

decline. By definition these individuals experience cognitive complaints, but these may be caused by various factors other than amyloid-β accumulation. For example, it has been shown that memory clinic SCD patients have higher (subclinical) depressive symptoms compared with community-dwelling individuals with SCD (Perrotin et al., 2016). In the present study, however, individuals with a current psychiatric diagnosis were excluded prior to enrolment. In addition, analyses were adjusted for depressive symptoms, which makes it unlikely that mental illness was responsible for the lack of associations. Nevertheless, irrespective of the nature of cognitive complaints, higher mean cortical amyloid-β load was associated with SCD related worries, and this appeared to be consistent for all cortical lobar regions.

It has been claimed that earliest changes in cognition are best perceived by the individual rather than by an observer (Caselli et al., 2014). In order to investigate the awareness of memory deficits, two discrepancy scores were calculated to adjust cognitive complaints for episodic memory performance (i.e., memory deficit awareness index) and informant reports (i.e., self-proxy index). Although SCD conceptually refers to the selfperception of cognitive decline and does not require confirmation by informants, we did not find different associations between amyloid-β and the self-proxy index. In line with a study on cognitively normal individuals from the community (Vannini et al., 2017), we found a positive relation between amyloid-β load and memory deficit awareness, which indicated that individuals with increased amyloid-β load showed heightened memory deficit awareness or hypernosognosia. We furthermore found that associations were dependent on the level of education. The previous indicates that when taking into account the degree of delayed memory recall with cognitive complaints this could result in a stronger relationship with amyloid-β load, particularly for individuals with relatively lower education levels. Earlier studies have used this index to investigate anosognosia, and showed that AD patients have an impaired memory deficit awareness, i.e., more severe episodic memory performance compared with self-rated cognitive performance (Barba et al., 1995; Perrotin et al., 2015). In the present study we found opposite patterns compared to patients with AD dementia (Perrotin et al., 2015). Although it needs to be interpreted with care, these positive associations could reflect a higher level of memory deficit self-awareness in individuals with elevated amyloid-beta accumulation (Vannini et al., 2017). The index scores seemed to be slightly driven by episodic memory performance, and our findings imply that higher amyloid-β load can be observed in cases when individuals' self-rated cognitive complaints are less severe than their episodic memory performance, which is in line with studies showing that episodic memory starts to deteriorate early in the disease course (Hanseeuw et al., 2017; Lim et al., 2018).

 First, while we have incorporated every available SCD item from our cohort, there may be other questionnaires which are better able to isolate SCD due to preclinical AD. On the other hand, the use of other SCD questionnaires may not provide very different results, because questionnaires will likely show high correlations, and typically inquire about memory complaints (Rabin et al., 2015). In addition, questionnaires rely on self-perception of cognitive decline, which in the present study did not show any relation with amyloid-β load or could have been distorted by other non-AD or subthreshold psychiatry SCD phenotypes (Buckley et al., 2015; Slot et al., 2018b). Notwithstanding, our results indicate that not SCD severity, but rather worries about self-perceived decline can be relevant. Second, we have not included individuals without cognitive complaints, therefore we cannot extrapolate our findings to the general population. On the other hand, we do expect that individuals who feel worried about their cognitive decline will most likely visit a memory clinic. Third, our memory self-awareness index seemed driven by relatively lower, but non-significant, episodic memory performance in individuals with higher amyloid-β burden. Lastly, the present study had a cross-sectional design. Therefore, it is not possible to make inferences as to whether individuals with more severe amyloid-β accumulation and SCD related worries will show an increased risk of clinical progression to symptomatic stages of AD. Future longitudinal studies are necessary to fully elucidate these associations, while taking into account the effects of concomitant AD pathology such as tau burden (measured with PET, CSF or blood) and hippocampal atrophy.

In conclusion, amyloid-β load was associated with SCD related worries and higher memory deficit self-awareness (i.e., hypernosognosia), but not with severity or specific pattern of cognitive complaints. Our findings indicate that worries about self-perceived decline may therefore help to identify amyloid-β related SCD.

### AUTHOR CONTRIBUTIONS

SV acquired, analyzed, and interpreted the data, drafted the manuscript, and approved the final content of the manuscript. TT, CvdW, LW, and RS acquired the data, critically revised the manuscript, and approved the final content of the manuscript. RO, SS, NP, AD, MY, BvB, and AL conceived and designed the study, analyzed and interpreted the data, drafted the manuscript and enhanced its intellectual content, and approved the final content of the manuscript. PS and WvdF conceived and designed the study, enhanced the intellectual content of the manuscript, and approved the final content of the manuscript.

### FUNDING

WvdF is recipient of a research grant for the SCIENCe project from Gieske-Strijbis Fonds. SV and RS are supported by a research grant from Gieske-Strijbis Fonds. SS is supported by grants from JPND Euro-SCD and Zon-MW (Off-Road).

The VUmc Alzheimer Center is supported by Alzheimer Nederland and Stichting VUmc Fonds. Research of the VUmc Alzheimer Center is part of the neurodegeneration research program of the Amsterdam Neuroscience. [18F]Florbetapir PET scans were made possible by Avid Radiopharmeuticals Inc. SV, RS, TT, LW, CvdW, SS, NP, AD, RO, AL, MY, and BvB report no conflict of interest. SP received grant support (for the institution) from GE Healthcare, Danone Research, Piramal and MERCK. In the past 2 years he has received consultancy/speaker fees from Lilly, GE Healthcare, Novartis, Forum, Sanofi, Nutricia, Probiodrug and EIP Pharma. All funding is paid to the institution. WvdF received grant support from ZonMW, NWO, EU-FP7, Alzheimer Nederland, CardioVascular Onderzoek Nederland, Stichting Dioraphte, Gieskes-Strijbis Fonds, Boehringer Ingelheim, Piramal Neuroimaging, Roche

### REFERENCES

BV, Janssen Stellar, Combinostics. All funding is paid to the institution.

## ACKNOWLEDGMENTS

We would like to acknowledge the participants of the SCIENCe cohort for dedicating their time and energy to this study.

### SUPPLEMENTARY MATERIAL

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fnagi. 2019.00007/full#supplementary-material

**Conflict of Interest Statement:** The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Copyright © 2019 Verfaillie, Timmers, Slot, van der Weijden, Wesselman, Prins, Sikkes, Yaqub, Dols, Lammertsma, Scheltens, Ossenkoppele, van Berckel and van der Flier. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

# Moderating Effect of Cortical Thickness on BOLD Signal Variability Age-Related Changes

Daiana R. Pur1 \*, Roy A. Eagleson1,2, Anik de Ribaupierre3 , Nathalie Mella3 and Sandrine de Ribaupierre1,4

1 School of Biomedical Engineering, Western University, London, ON, Canada, 2 Department of Electrical and Computer Engineering, Western University, London, ON, Canada, 3 Department of Psychology, University of Geneva, Geneva, Switzerland, 4 Department of Clinical Neurological Sciences, Schulich School of Medicine, Western University, London, ON, Canada

The time course of neuroanatomical structural and functional measures across the lifespan is commonly reported in association with aging. Blood oxygen-level dependent signal variability, estimated using the standard deviation of the signal, or "BOLDSD," is an emerging metric of variability in neural processing, and has been shown to be positively correlated with cognitive flexibility. Generally, BOLDSD is reported to decrease with aging, and is thought to reflect age-related cognitive decline. Additionally, it is well established that normative aging is associated with structural changes in brain regions, and that these predict functional decline in various cognitive domains. Nevertheless, the interaction between alterations in cortical morphology and BOLDSD changes has not been modeled quantitatively. The objective of the current study was to investigate the influence of cortical morphology metrics [i.e., cortical thickness (CT), gray matter (GM) volume, and cortical area (CA)] on age-related BOLDSD changes by treating these cortical morphology metrics as possible physiological confounds using linear mixed models. We studied these metrics in 28 healthy older subjects scanned twice at approximately 2.5 years interval. Results show that BOLDSD is confounded by cortical morphology metrics. Respectively, changes in CT but not GM volume nor CA, show a significant interaction with BOLDSD alterations. Our study highlights that CT changes should be considered when evaluating BOLDSD alternations in the lifespan.

Edited by: Beatrice Arosio, University of Milan, Italy

Reviewed by: Liang Gong, Chengdu Second People's Hospital, China Dejan Jakimovski, University at Buffalo, United States

## \*Correspondence:

Daiana R. Pur<EMAIL_ADDRESS>
Received: 04 November 2018 Accepted: 18 February 2019 Published: 12 March 2019

#### Citation:

Pur DR, Eagleson RA, de Ribaupierre A, Mella N and de Ribaupierre S (2019) Moderating Effect of Cortical Thickness on BOLD Signal Variability Age-Related Changes. Front. Aging Neurosci. 11:46. doi: 10.3389/fnagi.2019.00046 Keywords: signal variability, BOLD fMRI, structural alterations, cortical morphology, aging, cortical thickness, neural processing, biomarker

### INTRODUCTION

Normal aging is associated with marked functional and structural neuroanatomical alterations in cortical thickness (CT), gyrification, cortical surface area (CA), gray (GM), and white matter volume (WM) (Salat et al., 2009; Thambisetty et al., 2010; McGinnis et al., 2011; Hogstrom et al., 2013). Importantly, magnetic resonance imaging studies (MRI) show that the magnitude and rate of change of these cortical morphometry metrics is not constant across the cortex but rather it varies with age and brain region (Raz, 2005; Jiang et al., 2014; Storsve et al., 2014; Dotson et al., 2016), and is reported to accelerate with increasing age (Driscoll et al., 2009; Jiang et al., 2014). For example, a longitudinal study of alterations in cortical morphometry in older adults found accelerated changes with increasing

age in temporal and occipital cortices (Storsve et al., 2014). Furthermore, other studies report that the temporal lobes are most vulnerable to age-related morphometric changes, and that these changes reflect age-related cognitive impairment (Singh et al., 2006; Fjell et al., 2009; Pacheco et al., 2015). There is considerable evidence that neuroanatomical alterations reflect underlying functional alterations, especially in cognition (Fjell et al., 2006; Rossini et al., 2007; Ziegler et al., 2010; Achiron et al., 2013). In fact, functional magnetic resonance imaging (fMRI) studies, which rely on the blood oxygen level-dependent (BOLD) signal as a correlate of neuronal activity, report that changes in cortical morphology across adult lifespan impact the hemodynamic properties of the brain. For example, there are cortical laminar differences in BOLD signal (Tian et al., 2010; Huber et al., 2015), thicker cortical regions were reported to have a lower relative oxygen extraction fraction (Zhao et al., 2016). Therefore, since aging is associated with significant neuroanatomical alterations, these should be considered when assessing function (i.e., cognitive ability) using the BOLD signal.

The Standard Deviation of the BOLD signal can be used to estimate variability (hereafter, "BOLDSD"), and is believed to reflect the brain's dynamic ability to undergo fast moment-to-moment switching through network reconfigurations (Garrett et al., 2010, 2014; Grady and Garrett, 2014). It is an emerging index of cognitive health in aging, with higher regional BOLDSD being associated with enhanced performance on certain cognitive tasks (i.e., task switching) but not on others (i.e., distractor inhibition) (Armbruster-Genc et al., 2016; Guitart-Masip et al., 2016). Generally, increased BOLDSD is associated with younger age, faster and more consistent performance on cognitive tasks, and cognitive flexibility (Garrett et al., 2013a; Armbruster-Genc et al., 2016). The physiological mechanisms underlying BOLDSD remain largely unknown. For instance, decreased dopaminergic transmission is proposed to be associated with decreased BOLDSD in subcortical areas in older adults compared to younger ones (Guitart-Masip et al., 2016). Another study reported that higher BOLDSD is associated with superior pain coping capabilities (Rogachov et al., 2016). However, there are few studies investigating the interaction between age-related alterations in cortical morphology and BOLDSD. One study reported that increased microstructural integrity of WM pathways is associated with greater BOLDSD (Burzynska et al., 2015). Given that neuroanatomical alterations associated with normative aging are known to influence cognitive performance, and that they influence the BOLD signal, their impact as physiological confounds to BOLDSD should be investigated. This is particularly relevant because there is a considerable degree of inconsistency of methods used and results across studies investigating BOLDSD. In fact, some studies report greater regional BOLDSD in older adults (Garrett et al., 2010, 2011; Nomi et al., 2017), individuals with stroke (Kielar et al., 2016), multiple sclerosis (Petracca et al., 2017), Alzheimer disease (Makedonov et al., 2013, 2016; Scarapicchia et al., 2018) and other neurological disorders (Zöller et al., 2017).

The objective of this study is to investigate the contribution of cortical morphology (i.e., CT, CA, and GM volume) to age-related BOLDSD changes in older adults. A longitudinal framework, consisting of two scan points, should help reduce some of the inter-individual variance in neuroanatomy by accounting for external factors such as lifestyle, and various socio-economic and demographic factors. To this end, we investigated global and regional differences in BOLDSD in a group of older adults scanned twice at an interval of approximately 2.5 years, and regressed the effect of cortical morphology by introducing CT, CA, and GM as covariates. We hypothesized that cortical morphology metrics show an interaction with BOLDSD. We predict that age-related neuroanatomical alterations in CT, CA, GM are physiological confounds to BOLDSD measures, and that consequently adjusting for these metrics may help "unmask" the functional relevance of BOLDSD.

### MATERIALS AND METHODS

### Participants

All data obtained for the present study were obtained from the longitudinal Geneva Aging Study, after approval by the ethics committee of the Faculty of Psychology and Educational Sciences of the University of Geneva and the Swiss Ethic committee. Older subjects were initially recruited either from the University of the Third Age of Geneva or through newspaper and association advertisements for pensioners, as part of a larger longitudinal study. All participants gave written informed consent and older adults received a small amount of money as a compensation for their transportation fees.

Our initial sample consisted of 31 older adults scanned twice (mean age at first scan = 71.65 ± 6.03 years, mean age at second scan = 74.06 ± 5.99 years; 9 males). These subjects were chosen, within our pool, because they were the only ones that had undergone two T1 structural images and task fMRI scans, as well as other cognitive tests. Participants were screened for health problems with a questionnaire. The structural MRIs were inspected to rule out severe abnormalities (white matter changes, ventricular enlargement, tumors etc.). Three of the participants showed signs of Parkinson or lesion on their anatomical MRI, so they were excluded from the final sample (n = 28). All models and results in this current paper thus reflect 28 older adults (mean age at first scan = 71.61 ± 6.21 years, mean age at second scan = 74.07 ± 6.15 years; 7 males). The scans were 2.46 ± 0.69 years apart.

### MRI Data Acquisition

Participants were scanned in a Siemens Trio 3T magnet. A BOLD fMRI task-rest sequence was administered using a reaction time paradigm, where the participant had to indicate on which side a cross was changing into a square, as fast as possible (Mella et al., 2013). The task consisted of eight experimental blocks, interspersed with eight resting blocks (respectively, 52–20 s). The BOLD activity was obtained using an echo planar imaging acquisition (echo time, TE = 30 ms, time repetition, TR = 2100 ms, flip angle = 80◦ , field of view, FOV = 205 mm). Then, a structural T1-weighted MRI was acquired (TE = 2.27 ms, TR = 1900 ms, FOV = 256 mm, voxel size 1.0 mm × 1.0 mm × 1.0 mm).

### Structural Data Processing

fnagi-11-00046 March 8, 2019 Time: 17:21 # 3

Structural T1-weighted MR images were analyzed using Freesurfer version 6.0, a widely used and freely available automated processing pipeline1 , which allows surface-based three dimensional reconstruction and quantification of cortical morphology. The standard steps for analysis were implemented (using "recon-all" pipeline with the default set of parameters). Regional measures of GM, CT, and CA for each hemisphere were obtained using the automated anatomic parcellation procedure. Technical details are found in prior publications (Dale et al., 1999; Fischl et al., 1999a,b, 2001; Fischl and Dale, 2000; Zhang et al., 2001; Salat et al., 2004; Winkler et al., 2012).  GM and WM surface boundaries were reconstructed to estimate the distance between them across the cortex (Fischl and Dale, 2000). The generated cortical models were inflated into spheres to be registered to a spherical atlas and parcellated into regions of interest using Destrieux atlas (Fischl and Dale, 2000; Segonne et al., 2007; Destrieux et al., 2010).

### Functional Data Processing

Processing of the functional data were performed using FSL version 5.0 (Analysis Group, FMRIB, Oxford, United Kingdom (Smith et al., 2004). Standard preprocessing were followed using FSL's FEAT and FSL's Melodic for functional data (Jenkinson et al., 2012). Briefly, for each participant preprocessing steps included motion correction, slice timing, spatial normalization, highpass temporal filtering (100 s), smoothing (kernel 5 mm FWHM), and linear registration (12 degrees of freedom) of the functional data to the high-resolution T1 structural image, and from T1 to 1 mm standard space (MNI 152). Additionally, FSL's Melodic was used to correct each functional image for artifacts using automatic dimensionality estimation via independent component analysis (ICA) (Beckmann and Smith, 2004). Change in cortical morphology between the two scanning sessions was determined as cortical morphology metrics GM (delta.volume), CT (delta.thickness), CA (delta.area) at timepoint 2–at timepoint 1.

### BOLD Signal Variability Calculation

As part of the Geneva Dataset, the subjects were performing different cognitive tasks, and for the current study, only the fixation/rest blocks from the block design task fMRI were selected to calculate BOLDSD, as previously described (Garrett et al., 2010). BOLDSD analysis was restricted to the GM using participant specific GM mask obtained from FSL's FAST. First, fixation blocks were normalized so that the overall four-dimensional mean across brain and block was 100. Next, for each voxel, the block mean was subtracted to remove block-wise drift, followed by concatenation of all blocks. The standard deviation of the normalized mean of the concatenated fixation blocks was used to obtain BOLDSD values for each brain region (n = 148) of each subject, as

1http://surfer.nmr.mgh.harvard.edu/

defined by the Destrieux Atlas (Destrieux et al., 2010), using in-house MATLAB code. Change (delta.variability) in variability between the two scanning sessions was calculated as variability at timepoint 2–variability at timepoint 1. BOLDSD encompassing both timepoints was introduced as "variability" (see section "Regional Model").

### Statistical Analysis

Linear mixed effects models (LMMs) were used to investigate the potential confounding effect of cortical morphology, GM, CT, CA on BOLDSD (Bates, 2005; Zuur et al., 2011).The LMMs allow estimation of the effects of explanatory variables ("fixed effects") and their interactions on the dependent variable (i.e., BOLDSD), while statistically controlling for the effects of randomly selected participants ("random effects") on the dependent variable (BOLDSD). Multiple models were run and the likelihood-ratio test was used to (1) investigate if introducing subjects as random effects improves the fit of the model (2) to select the optimal combination of fixed effects fitted with maximum likelihood, while keeping the random effects structure the same. Therefore, the likelihood-ratio test via ANOVA was used to compare the goodness of fit of different models. R statistical software package (R Core Team, 2013) 2 was used for all statistical analyses. Correlation between cortical morphology measures were computed using cor function in R. All models were fitted using the "lmer" or "lm" function in R. "lmerTest" R package was used to obtain summary table and p-values for linear mixed models via Satterthwaite's degrees of freedom method (Kuznetsova et al., 2017). A spatiotemporal approach to LMMs allowed characterization of regionally specific variation across the brain (Bernal-Rusiel et al., 2013). This approach was implemented to investigate if there is a significant change in BOLD variability across time (1) all cortical regions (2) region specific. Random effect structure with subjects varying in their "baseline" BOLD variability was retained (random intercept, 1| ID). Additionally, to model a different rate of change in the expected response levels, time varying predictors were introduced by random slopes (i.e., thickness, time) ("Regional Model"). To reduce spatial correlation issues an LMM model with the described structure was applied at each spatial location (i.e., region of interest) independently. Each model produced a parameter which quantifies the mean change in BOLD variability (delta.variability) for that region. P-values were corrected for multiple comparisons using false discovery rate (FDR) at q = 0.05 (Benjamini and Yekutieli, 2001).

### Models

Model 1 < lm (delta.variability∼ region).

Model 2 < - lmer [delta.variability∼ region+(1| ID), REML = F]. Model 3 < - lmer [delta.variability ∼ delta.thickness + region+(1| ID), REML = F].

Model 4 < - lmer [delta.variability ∼ delta.area + region + (1| ID), REML = F].

2www.R-project.org

Model 5 < - lmer [delta.variability ∼ delta.thickness + delta.area + region+(1| ID), REML = F].

Model 6 < - delta.variability ∼ delta.volume + region + (1 | ID).

### **Final Model**

lmer [delta.variability∼ delta.thickness + region+(1| ID)].

**Regional Model**

lmer [variability∼ time + thickness + (1+thickness + time| ID)].

### RESULTS

### Relations Between Cortical Thickness and BOLD Signal Variability Age-Related Changes

The functional and structural data of 28 participants was assessed. As expected, our study showed that CA and GM are highly correlated r = 0.904, p < 0.001 (95% Cl 0.901–0.908) and consequently collinearity was suspected. Multiple LMMs were utilized to assess the effect of neuroanatomical metrics CT, CA, and GM on BOLDSD age-related changes. Results from the linear mixed effect models run with likelihood-ratio test via ANOVA, are presented in **Table 1**. (A) indicates that subject intercept should be introduced as a random effect, while (B–E) show the steps that have led to the final model. Specifically, **Table 1**. (B,C) indicate that introducing CT or CA as covariates, separately, each significantly improve the fit of the model p < 0.0001, p < 0.05, respectively. However, from (D) it is apparent that adding CA to a model that already has CT as a covariate does not improve the fit of the model, meaning that CT only should be included in the final model (see section "Final Model"). (E) indicates that introducing GM as a covariate does not improve the fit of the model. Neither CT, GM, nor CA mean changes were significant as tested with LMMs. Regional Model LMM indicated that neither overall nor regionally specific mean BOLDSD change was significant after FDR correction.

## DISCUSSION

## Cortical Thickness and Its Association With BOLDSD

In this study we aimed at determining the contribution of cortical morphology to BOLDSD in order to better understand

AIC, akaike information criterion; BIC, Bayesian information criterion; Chisq Chi, chi-square test statistic; Df, degrees of freedom; logLik, log-likelihood; Pr > Chisq. P < 0.05. ID represents subject identification number.

the physiological nature of brain function capture by BOLDSD. BOLDSD changes across the lifespan have been shown to be robust to certain vascular factor such as cerebral blood flow, BOLD cerebrovascular reactivity, maximal BOLD signal change (Garrett et al., 2017), as well as GM volume changes (Nomi et al., 2017). However, using LMMs, we found that functional alterations in aging as captured by BOLDSD are confounded by the structural metric CT. This is not surprising, considering that it is well known that BOLD signal change/activation is dependent on the laminar organization of the cortex, and consequently is influenced by its depth or thickness (Koopmans et al., 2010; Tian et al., 2010; Bandettini, 2012). In fact, neurovascular coupling is reported to vary by cortical depth and layer (Goense et al., 2012).

In a longitudinal study investigating CT, GM volume, CA across the lifespan, Storsve et al. (2014) reported cortical morphology metric specific and region specific rates of mean annual percentage change (APC) in healthy adults aged 23–87 years. In most regions, GM volume has a mean APC of –0.51, CT of –0.35, and CA of –0.19. Other longitudinal studies in older adults, report similar reductions: in GM volume, mean APC ranging from –0.5 to –2.1 ± 1.6% (Tang et al., 2001; Fjell et al., 2009), in CT, mean APC –0.3% (Shaw et al., 2016), in WM tracts, mean APC ranging from – 0.20 to –0.65 depending on the tract location (Storsve et al., 2016). In our sample, the change in cortical morphology from scan 1 to scan 2 (2.5 years apart) was not statistically significant. However, the findings reported by these longitudinal studies suggest that while the neuroanatomical alterations may be too subtle to reach statistically significance in the investigated timespan of 2.5 years, they may still contribute to BOLDSD age-related findings. Particularly, accounting for CT age-related changes may help "unmask" the functional value of BOLDSD.

While the relationship between cortical morphology metrics in aging is dynamic, GM volume changes were largely accounted by changes in CT rather than CA, highlighting the importance of tracking changes in CT (Storsve et al., 2014). In fact, studies show that CT and CA are genetically independent (Panizzon et al., 2009). Their neurodevelopment in the lifespan is largely independent of each other suggesting that they should be considered as separate metrics with different contributions to cortical volume (Im et al., 2008; Winkler et al., 2010; Eyler et al., 2011; Lemaitre et al., 2012; Storsve et al., 2014). Furthermore, studies show that that some cortices experience decrease in cortical morphology metrics at a higher rate than others (Storsve et al., 2014).

The hemodynamic properties of each brain region are highly correlated with its cortical structure (Ulrich and Yablonskiy, 2016; Wen et al., 2018). Studies based on quantitative gradient recalled echo (qGRE) analysis use the GRE signal decay rate parameter (R2t∗ ) to gather information on tissue-specific contributions to the fMRI signal. Regional R2t∗ metric variations are associated with variations in neuronal density and myelination (positive correlation), as well as glial cells and synapses concentration (i.e., negative correlation) (Wen et al., 2018). In normal aging R2t∗ increases in all cortical regions (Zhao et al., 2016). Thicker regions show the opposite pattern, respectively, decreased neuronal density, and higher concentration of glial cells and synapses relative to neurons. Thicker areas also tend to extract less oxygen from the blood, as measured by oxygen extraction ratios (i.e., expressed as local-to-global ratio). These findings indicate that laminar differences in cellular content impact neurovascular coupling mechanisms, which in turn may compromise the power of BOLDSD measurements to detect "real" changes in neuronal variability processing (Harris et al., 2011). Although, laminar differences in BOLDSD remain rather elusive, our study suggests the that CT should be considered in BOLD variability studies.

## Age-Related Changes in BOLDSD

In our study, we did not find a significant change in BOLDSD, likely due to the low timespan between scans (2.5 years), and relatively low sample (n = 28). Overall, most studies reporting a decrease in BOLDSD suggest that this finding may indicate structural reductions in synaptic complexity and integrity, as well as functional decline in neural optimization and flexibility (Garrett et al., 2010). Burzynska et al. (2015) reported a positive correlation between increased microstructural integrity of WM and BOLDSD in healthy adults, consequently warranting the consideration of structural alterations in variability studies. Nomi et al. (2017) found increases in variability in bilateral anterior insula, anterior cingulate and ventral temporal cortex, and decreases in BOLDSD in sensory brain regions and other subcortical areas, in participants with ages ranging from 6 to 86 years. In their study they accounted for GM volume but not CT or CA. Two seminal studies investigated BOLDSD differences in aging alone (Garrett et al., 2010) and in relation to performance on cognitive tasks (Garrett et al., 2011) between a young group of participants (20–30 years) and an older one (56–85 years) and found both increases and decreases in regional variability with younger age alone, and younger age and better performance (Garrett et al., 2011) making it rather difficult to isolate specific key contributing regions. Most regions in these two studies showed the same trend but there were some inconsistencies. For example, superior frontal gyrus was reported to show greater variability with younger age and better performance (Garrett et al., 2011) in one study, while another one reported that its variability increases with age (Garrett et al., 2010). CT was not considered in any of these studies. The relationship between cognitive performance and/or flexibility and BOLDSD is definitely complex and task dependent. Behavioral studies indicate age-related differences in intra-individual variability on cognitive performance tests involving reaction time, and working memory. Older adults showing higher intra-individual variability on reaction time tests than younger adults, while the opposite is observed on working memory tests (Fagot et al., 2018). Additionally, the relationship between BOLDSD change, aging and cognitive health has not been a consistent finding. Some studies have found increased BOLD in healthy older adults (Baum and Beauchamp, 2014), Alzheimer's (Scarapicchia et al., 2018), attention deficit hyperactivity disorder (ADHD) (Nomi et al., 2018), and other neurological disorders (Zöller et al., 2017).

### Other Possible Confounds in BOLDSD Studies

Most studies investigating BOLD variability utilized a cross-sectional design while we used a within-subject design. This design is a highlight of our study because it allows for the investigation of normative age-related differences, specifically intra-individual effects of the processing of aging on cortical morphology and BOLDSD, rather than process of aging simply age differences across groups. Importantly, the within-subject design helps account for numerous sources of individual differences that may affect BOLD variability such as differences in dopaminergic neurotransmission (Guitart-Masip et al., 2016; Alavash et al., 2018), significant differences in vascular features, pain sensitivity and coping (Rogachov et al., 2016), clinical symptomology (Ke et al., 2015; Nomi et al., 2018; Zöller et al., 2018), and even differences in tendency for financial risk taking (Samanez-Larkin et al., 2010). Given that in our study the participants were scanned at an interval of approximately 2.5 years, we were able to at least partially account for such factors. Furthermore, it is clear from the literature that there are numerous environmental factors [i.e., socioeconomic status, education (Chan et al., 2018)] that may contribute to individual neuroanatomical alterations, which in turn may confound BOLD variability studies. This further supports the findings of our study, specifically that CT is a neuroanatomical metric that should be accounted for.

Lastly, there may be inconsistency in results between studies due to: (1) using task-fMRI and resting-state fMRI, (2) calculation of BOLD signal variability using standard deviation vs. mean-square successive difference) (for review, Garrett et al., 2013b), (3) type of statistical analysis performed (e.g., partial least squares method, LMM, general linear model), and (4) not accounting for contribution of CT and the other mentioned sources of individual differences.

### STUDY LIMITATIONS

The main limitations, as discussed above and further addressed by Garrett et al. (2013b); Scarapicchia et al. (2018), are the lack of standardization in acquiring and analyzing the fMRI data. Additionally, the sample used in our study consists of a relatively small sample of older adults and rather short scan-rescan time, consequently this limits our ability to make strong generalization to other age groups or longer time spans, respectively. Nevertheless, since studies show that the brain

### REFERENCES

undergoes extensive annual structural alterations across the lifespan at different rates, our finding that CT contributes to BOLDSD alterations, remains an important consideration.

### CONCLUSION

In conclusion, contrary to a view that BOLD variability is just "noise," we consider it to be emerging as an important metric of normal aging. Keeping in mind that across the lifespan there are considerable cortical morphometry alterations and that cortical depth affects the BOLD signal, we reported that CT contributes to BOLDSD changes, in an older sample of health adults. An increasing number of studies are considering healthy regional BOLDSD changes in the context of functional networks (Rogachov et al., 2016; Nomi et al., 2017). Pursuing this direction while accounting for CT and other possible confounding factors such as dopaminergic neurotransmission, socioeconomic background etc., should reveal new insights into the mechanisms behind age-related neural processes.

### DATA AVAILABILITY

All datasets generated for this study are included in the manuscript and/or the supplementary files.

### AUTHOR CONTRIBUTIONS

DP, RE, and SR conceived and designed this specific project on the data collected by NM and AR. DP analyzed the data. All co-authors participated in writing of the manuscript.

## FUNDING

This work was supported by the SNF (Swiss National Foundation, Grant Nos. 100014\_135410, PMPDP1 171335, and PMPDP1 158319).

### ACKNOWLEDGMENTS

We thank the participants of the Geneva Aging Study. We also thank the Department of Statistical and Actuarial Sciences at Western University for helpful discussions and advice on data analysis and statistical modeling.

functional connectome during cognitive performance. Neuroimage 172, 341– 356. doi: 10.1016/j.neuroimage.2018.01.048

and gray matter volume? Neurobiol. Aging 33, 617.e1–617.e9. doi: 10.1016/j. neurobiolaging.2010.07.013

fnagi-11-00046 March 8, 2019 Time: 17:21 # 8

**Conflict of Interest Statement:** The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Copyright © 2019 Pur, Eagleson, de Ribaupierre, Mella and de Ribaupierre. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

# The Combination of DAT-SPECT, Structural and Diffusion MRI Predicts Clinical Progression in Parkinson's Disease

#### Sara Lorio1,2,3 \*, Fabio Sambataro2,4, Alessandro Bertolino2,5, Bogdan Draganski3,6 and Juergen Dukart2,7,8

1 Developmental Neurosciences, UCL Great Ormond Street Institute of Child Health, University College London, London, United Kingdom, 2 Roche Pharma and Early Development, Neuroscience, Ophthalmology and Rare Diseases, F. Hoffmann-La Roche Ltd., Basel, Switzerland, 3 Laboratory for Research in Neuroimaging, Department of Clinical Neurosciences, Lausanne University Hospital, University of Lausanne, Lausanne, Switzerland, 4 Department of Experimental and Clinical Medical Sciences, University of Udine, Udine, Italy, 5 Department of Basic Medical Science, Neuroscience and Sense Organs, University of Bari, Bari, Italy, 6 Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany, 7 Institute of Neuroscience and Medicine, Brain and Behaviour (INM-7), Research Centre Jülich, Jülich, Germany, 8 Institute of Systems Neuroscience, Medical Faculty, Heinrich Heine University Düsseldorf, Düsseldorf, Germany

### Edited by:

Beatrice Arosio, University of Milan, Italy

### Reviewed by:

Francesca Baglio, Fondazione Don Carlo Gnocchi Onlus (IRCCS), Italy Carlo Abbate, IRCCS Ca'Granda Foundation Maggiore Policlinico Hospital (IRCCS), Italy

> \*Correspondence: Sara Lorio<EMAIL_ADDRESS>
Received: 12 November 2018 Accepted: 26 February 2019 Published: 15 March 2019

#### Citation:

Lorio S, Sambataro F, Bertolino A, Draganski B and Dukart J (2019) The Combination of DAT-SPECT, Structural and Diffusion MRI Predicts Clinical Progression in Parkinson's Disease. Front. Aging Neurosci. 11:57. doi: 10.3389/fnagi.2019.00057 There is an increasing interest in identifying non-invasive biomarkers of disease severity and prognosis in idiopathic Parkinson's disease (PD). Dopamine-transporter SPECT (DAT-SPECT), diffusion tensor imaging (DTI), and structural magnetic resonance imaging (sMRI) provide unique information about the brain's neurotransmitter and microstructural properties. In this study, we evaluate the relative and combined capability of these imaging modalities to predict symptom severity and clinical progression in de novo PD patients. To this end, we used MRI, SPECT, and clinical data of de novo drug-naïve PD patients (n = 205, mean age 61 ± 10) and age-, sex-matched healthy controls (n = 105, mean age 58 ± 12) acquired at baseline. Moreover, we employed clinical data acquired at 1 year follow-up for PD patients with or without L-Dopa treatment in order to predict the progression symptoms severity. Voxel-based group comparisons and covariance analyses were applied to characterize baseline disease-related alterations for DAT-SPECT, DTI, and sMRI. Cortical and subcortical alterations in de novo PD patients were found in all evaluated imaging modalities, in line with previously reported midbrainstriato-cortical network alterations. The combination of these imaging alterations was reliably linked to clinical severity and disease progression at 1 year follow-up in this patient population, providing evidence for the potential use of these modalities as imaging biomarkers for disease severity and prognosis that can be integrated into clinical trials.

Keywords: Parkinson's disease, voxel-based morphometry, voxel-based quantification, covariance analysis, symptoms severity

### INTRODUCTION

fnagi-11-00057 March 13, 2019 Time: 18:15 # 2

Parkinson's disease (PD) is primarily characterized by progressive accumulation of aggregated α-synuclein in the brainstem, leading to a degeneration of dopaminergic neurons in substantia nigra (Lang and Lozano, 1998; Xu et al., 2002; Braak et al., 2003; Kraemmer et al., 2014). This presumably toxic accumulation induces a progressive loss of dopaminergic input to the striatum and further degeneration of striato-cortical pathways, resulting in the occurrence of different motor and non-motor symptoms (Houk and Wise, 1995; Lang and Lozano, 1998; Kraemmer et al., 2014). Current PD drugs focus on the symptoms treatment, however, the main goal of pharmaceutical research is to develop drugs able to slow or even stop the clinical progression.

To improve the monitoring of disease progression and the evaluation of drug effectiveness, it is essential to identify biomarkers able to detect the neurodegenerative alterations at all circuitry levels. Such biomarkers should demonstrate a strong and reproducible correlation with pathological changes and symptoms severity in multicenter studies (McGhee et al., 2013).

Several imaging modalities have been suggested for that purpose in the literature. Dopamine transporter single photon emission tomography (DAT-SPECT) provides a semiquantitative assessment of striatal dopaminergic deafferentation. It is a well-established diagnostic biomarker of PD, owing to the strong correlation between the amount of dopamine transporters in the striatum and the number of dopaminergic neurons in substantia nigra (Kraemmer et al., 2014). However, DAT-SPECT does not provide information on non-dopaminergic disease aspects, and its link to disease progression in de novo PD patients remains unclear (Kägi et al., 2010).  Several sMRI studies reported volumetric changes in PD using voxel-based morphometry (VBM) analysis (Burton et al., 2004; Nagano-Saito et al., 2005; Wattendorf et al., 2009). However, only few studies reported the association between volumetric changes or cortical thinning and PD symptoms severity, measured by neuropsychological scores for testing attention and memory, and olfactory alterations (Junqué et al., 2005; Camicioli et al., 2011; Melzer et al., 2012; Segura et al., 2014; Campabadal et al., 2017).  However, most of these studies measured structural changes in small, heterogeneous PD patient cohorts (e.g., highly varying disease duration), resulting in variable magnitude and directionality of the respective findings (McGhee et al., 2013; Meijer et al., 2013; Weingarten et al., 2015). Moreover single DTI measures failed to accurately assess disease severity and prognosis, hampering their application in standard clinical practice or as biomarkers in clinical trials (McGhee et al., 2013; Pyatigorskaya et al., 2014; Atkinson-Clement et al., 2017). In contrast, in a small proof-of-concept study in PD patients, multimodal MRI measures based on DTI and R2∗ maps have been shown to provide complementary information allowing for a better differentiation of patients and controls (Peran et al., 2010). However, the relative and combined value of MRI measures and DAT-SPECT as biomarkers of disease severity and prognosis remains unknown.

Despite imaging changes in individual brain regions can be used as diagnostic biomarker, theories on neuro-degenerative disease increasingly focus on the role of brain network alterations as potential predictors of early disease severity and prognosis (Helmich et al., 2010; Woo et al., 2017). In fact previous studies showed that PD targets regions that, in healthy individuals have a well-defined correlation patterns (Helmich et al., 2010; Woo et al., 2017). However, the interaction between changes in the striatal dopamine and the morphological and microstructural alterations in the striato-cortical circuits remains unclear. This interaction can be evaluated using structural covariance analysis, which examines whether an imaging measure in one region correlates with the variation of the same, or other modalities measures in other brain regions (Mechelli et al., 2005; Schmitt et al., 2009; Alexander-Bloch et al., 2013a). Complementary to functional MRI (fMRI) and DTIbased connectomics, population covariance in brain anatomy represents another source of information about inter-regional anatomical associations (Alexander-Bloch et al., 2013b). A crucial difference between fMRI, diffusion MRI networks, and structural covariance is that the latter estimates inter-regional correlations based on group of images, while the first two networks can be constructed from connectivity measures computed for an individual image (Alexander-Bloch et al., 2013a,b). Another challenge of the structural covariance networks might be the biological interpretation of the results (Alexander-Bloch et al., 2013a). However, recent studies showed that the structural covariance pattern are influenced by synaptic connectivity between brain regions, genetic and developmental relationships, and different degenerative processes (Gong et al., 2012; Chou et al., 2015; Chang et al., 2017; de Schipper et al., 2017; Oosterwijk et al., 2018; Yee et al., 2018). Therefore, we applied this method to investigate structural network alterations using MRIbased measurements and striatal dopamine transporter uptake derived from DAT-SPECT.

Here, we evaluated DAT-SPECT, DTI and sMRI as biomarkers of disease severity and progression in de novo PD patients. Disease severity was assessed longitudinally through the modified version of the unified Parkinson's disease rating scale (MDS-UPDRS) evaluated at baseline (same time point at which the imaging data was acquired) and at 1 year follow-up. We used well-established VBM, voxel-based quantification (VBQ) (Draganski et al., 2011) and structural covariance analyses to identify disease-related alterations in a large cohort of de novo PD patients. We then tested whether combinations of imaging alterations identified in drug-naïve de novo PD were associated with current clinical severity measured by baseline MDS-UPDRS scores. Moreover, we employed those imaging measure to predict the future disease progression quantified by the MDS-UPDRS variations between the two time points.

As the dopaminergic treatments taken by some patients after the baseline evaluation have substantial effects on the clinical symptoms measured with MDS-UPDRS, we accounted for those effects by applying the prediction models separately to the group of patients receiving medication and to the one without treatment employing off medication clinical assessment (Mueller et al., 2018; van Ruitenbeek et al., 2018).

### MATERIALS AND METHODS

### Subjects

Data of de novo PD patients (N = 205) and healthy controls (HC) (N = 105) included in this study were obtained from the Parkinson's Progression Markers Initiative (PPMI) database1 . PPMI is a large multicenter study and each site independently received ethics approval of the protocol. This study was carried out in accordance with Good Clinical Practice (GCP) regulations and International Conference on Harmonization (ICH) guidelines. Written informed consent was obtained from all participants in accordance with the Declaration of Helsinki.

All subjects underwent sMRI, DTI, and DAT-SPECT at baseline, and had MDS-UPDRS 1, 2, 3, and total, acquired at baseline (BL) and after 1 year (TP1). De novo PD patients included in the PPMI database had a diagnosis confirmed by DAT-SPECT scans and Hoehn and Yahr stage I or II. The exclusion criteria were diagnosis of dementia, psychiatric disorders or other neurological disease detectable with MRI at baseline. At BL all patients were drug-naïve, while at TP1 some patients started drug treatment with L-Dopa, dopamine agonist, and other unspecified medications. Only categorical (yes/no) information was recorded on the respective treatment categories (L-Dopa, dopamine agonists or other PD medication) without information about the dosage. In the current study, we included patients remaining without drug treatment at TP1 (called as PD no med) and patients taking only L-Dopa treatment at TP1 (called as PD on med). The PD on med group was composed of patients having the MDS-UPDRS3 evaluation performed off medication (patients were asked not to take L-Dopa for 24 h before the evaluation). From the 205 patients, we identified 56 subjects for the PD no med group, and 44 subjects for the PD on med group.

### Image Acquisition and Processing

Whole-brain MRI was performed using standardized protocols on different 3T scanners. All acquisition protocols included a standard T1-weighted MPRAGE sequence (TI/TR = 900/2,300 ms, TE = 2.98 ms, 1 mm isotropic resolution), and a 2D single-shot echo-planar DTI sequence for diffusion weighted images (TR/TE = 900/88 ms, 2 mm isotropic resolution, diffusion weighting along 64 gradient directions, b-value = 1000 s/mm2 ).

DAT-SPECT images were obtained using different camera systems. Details about SPECT image reconstruction are available on the PPMI website (see footnote 1) (reconstructed image matrix 91 × 109 × 91, 2 mm isotropic resolution).

1www.ppmi-info.org

VBM analysis was performed on the T1-weighted images using automated tissue classification and enhanced subcortical tissue probability maps embedded into the unified segmentation framework of SPM12 (Ashburner and Friston, 2005; Lorio et al., 2016). Image registration to the MNI space was performed for each subject applying subject-specific diffeomorphic estimates obtained using DARTEL (Ashburner, 2007) with default settings on the gray and white matter tissue maps (respectively, GM and WM). The warped GM maps were scaled by the Jacobian determinants of the deformation fields to account for local compression and expansion due to linear and non-linear transformation (Ashburner and Friston, 2000), resulting in GM volume maps. The GM volume maps were then smoothed using an isotropic Gaussian kernel of 6 mm full width at half maximum (FWHM).

The DTI maps were calculated from the diffusion weighted data using a group of libraries called TEEM2 . The pre-processing steps for the estimation of FA and MD maps included correction for distortions due to eddy currents and head motion (Rohde et al., 2004; Tao et al., 2009), and affine registration of the corrected diffusion weighted data to the T1-weighted images using FLIRT from FSL5.03 (Greve and Fischl, 2009).

For VBQ analysis, FA and MD maps were warped into MNI space using a non-linear registration approach based on the subject-specific diffeomorphic estimates (Ashburner, 2007), derived for the GM and WM maps without scaling by the Jacobian determinants. In order to enhance the specificity of the warped FA and MD values for brain tissue classes, we used the combination of weighting procedure with the GM and WM probability maps derived from the T1-weighted data, and Gaussian smoothing with a 6 mm FWHM isotropic smoothing kernel as described by Draganski et al. (2011). Separate FA and MD maps were generated for GM and WM sub-spaces.

DAT-SPECT data pre-processing was performed within SPM12 and included normalization to an average size DAT-SPECT template with subsequent normalization into the MNI space performed using the "normalization" function. Then the images were non-linearly warped from the MNI to the native space of the T1-weighted data using the spatial transformation parameters estimated for the GM and WM probability maps. This allowed the correction for partial volume effects using the modified Müller-Gartner method (Müller-Gärtner et al., 1992; Rousset et al., 1998) based on the convolution of the DAT-SPECT data with the tissue classification maps estimated from T1 weighted images. The GM SPECT images were then normalized to MNI space using parameters derived from T1-weighted data and scaled to the global mean GM signal for each subject. Finally, the SPECT images were smoothed with an isotropic Gaussian kernel of 6 mm FWHM.

### Statistical Analysis and Voxel-Based Group Comparison

We used odds ratios and t-test, as implemented in Matlab 2012b, to assess gender and age differences between PD patients and

2http://teem.sourceforge.net/index.html

3www.fmrib.ox.ac.uk/fsl

HC, and between PD on med and PD no med groups (**Table 1**). We compared the values of MDS-UPDRS scales (1, 2, 3, and total) measured at BL, TP1 and the differences between the two time points, using two-sample t-tests between the respective groups (see **Table 1**). Only MDS-UPDRS 3 values measured off medication were compared at TP1. Fisher's exact test was used to compare the disease dominant side between PD on med and PD no med subgroups (see **Table 1**).

To test for local differences in GM volume maps, FA and MD between PD patients and HC, we used SPM12 to perform a voxelwise t-statistic separately for every map and tissue class (GM and WM), using a flexible factorial design, controlling for sex, age, total intracranial volume (TIV) and image acquisition site. These tests were carried out using explicit masks defining GM and WM voxels. The masks were generated as follows: smoothed (FWHM of 6 mm isotropic), Jacobian-modulated tissue probability maps in MNI space were averaged across all subjects for each tissue class (GM and WM). Then binary masks were generated by applying a threshold of 20%, voxels for which neither the GM nor the WM probability exceeded that value were excluded from the analysis. This approach was used to ensure that each voxel was analyzed in only one subspace and that non-brain tissue was excluded.

Similarly, differences in DAT-SPECT were evaluated using voxel-based comparisons, restricting analyses to GM voxels and controlling for age and sex. We applied to all voxel-wise statistical analyses a family-wise error (FWE) corrected cluster threshold of p < 0.05 combined with a voxel-wise p < 0.001. We further estimated the effect size (Cohen's d) of the observed differences.

To test for differential interaction between striatal dopamine transporter uptake derived from DAT-SPECT and MRI-based measurements, we performed voxel-wise covariance analyses (Mechelli et al., 2005) between DAT-SPECT values in the putamen (region showing strongest differences between PD patients and HC) and GM volume, FA and MD maps. The putamen was delineated according to the basal ganglia human area template (Prodoehl et al., 2008). We computed separate covariance analyses for each MRI-based measure (GM volume, FA, MD) and each tissue type (gray and white matter for FA and MD), controlling for age, sex, TIV, disease dominant side and image acquisition site. The covariance analyses were carried out using GM and WM masks obtained as previously described.

An FWE-corrected cluster threshold of p < 0.05 combined with a voxel-wise p < 0.001 was applied for all analyses. To quantify the differential inter-modality correlations identified in the covariance analyses, we computed Spearman correlation coefficients between the putamen DAT-SPECT signal and the average value of the age and sex adjusted MRI-based measures showing significant between-group differences in the covariance analysis.

### Prediction Models: Symptoms Severity and Disease Progression

To evaluate the combined value of imaging measures as biomarkers of symptoms severity we used multiple linear regression models to predict each MDS-UPDRS subscale. The MDS-UPDRS 1, 2, 3, and total were the dependent variables, while the models regressors were the mean imaging measures extracted from regions showing significant differences between PD patients and HC (group differences: FA in pons nuclei, prefrontal MD, DAT signal in putamen; covariance differences: prefrontal GM volume, brainstem MD). For regions identified in covariance analyses we included in the model an interaction

HC, healthy controls; PD, Parkinson's disease patients, PD no med, patients without medications 1 year after baseline; PD on med, patients on L-Dopa treatment 1 year after the baseline. Bold characters and "∗ " indicate a significant group difference (p < 0.05).

term between the MRI measure and DAT-SPECT. All analyses were controlled for age, sex, and symptoms dominant side. The goodness of fit for each model was assessed using the determination coefficient (R 2 ), while the model overfit was evaluated by means of the leave-one-out adjusted R 2 coefficient. Additionally, we estimated the Pearson correlation coefficient between each of the above-mentioned imaging measures and the MDS-UPDRS 1, 2, 3, and total separately.

To test if the BL imaging measures were prognostic biomarkers of disease progression, we used multiple linear regression models for predicting the changes in MDS-UPDRS scores (1MDS-UPDRS) between TP1 and BL. The 1MDS-UPDRS 1, 2, 3, and total were the dependent variables, while the model regressors were the FA values in pons nuclei, prefrontal MD, DAT signal in putamen, and the interaction between putamen DAT signal and prefrontal GM volume, brainstem MD, respectively. The longitudinal prediction models were computed using data of both PD no med and on med groups. Patients receiving dopamine agonists at TP1 were not included in this analysis due to the longer wash out time of those treatments and their potential effect on the off medication assessment of symptoms severity.

To account for the effect of L-Dopa treatment on the prediction models, we performed separate regression analyses for the PD on med and no med groups. Demographic and clinical details of the two subgroups are provided in **Table 1**. Moreover we computed the Pearson correlation coefficient between each of the imaging measures and the 1MDS-UPDRS 1, 2, 3, and total separately for the PD on med and no med groups.

The multiple regression models for each MDS-UPDRS and 1MDS-UPDRS subscales were considered as significant at p < 0.05. In case of a significant multiple regression model, single regressors were considered as significant at p < 0.01. The Pearson correlation coefficients were corrected for multiple comparison using the false discovery rate (FDR) at q < 0.05.

### RESULTS

### Demographic and Clinical Variables

Patients and HC were matched for gender and age, as shown in **Table 1**. The groups PD on med and PD no med did not differ with respect to sex, age, and disease dominant side (see **Table 1**). All clinical scores were significantly higher for patients as compared to HC (**Table 1**). The group PD on med showed significantly higher MDS-UPDRS 1, 2, and total scores at BL and TP1, as well as higher 1MDS-UPDRS1 and 1MDS-UPDRS2 in comparison to the PD no med group (**Table 1**). No significant differences were found for the MDS-UPDRS3 at BL, TP1, and 1MDS-UPDRS3 between PD on med and no med groups.

### Voxel-Based Group Comparison

In PD patients significantly higher FA values were found in the brainstem WM corresponding to the pontine tegmentum (**Table 2** and **Figure 1a**). Increased MD values were found in patients with respect to HC in the operculum (**Table 2** and **Figure 1b**). No significant differences between patients and HC were observed for GM volume.

In the structural covariance analyses, we found significant differences in the correlation between DAT-SPECT in the putamen and MD values in the pons nuclei across patients and HC. The Spearman correlation coefficients showed a significant positive correlation in PD patients (ρ = 0.25; p < 0.001), and a significant negative one in HC (ρ = −0.52; p < 0.001) (**Figures 2a,b**).

Furthermore, we found significant differences between patients and HC in the covariance analysis evaluating the association between DAT-SPECT signal in the putamen and GM volume in the left prefrontal, premotor cortex and in the insula. We observed a significant positive correlation (ρ = 0.22; p < 0.001) for patients, and a significant negative correlation for controls (ρ = −0.44; p < 0.001) (**Figures 2c,d**). No significant differences were found for structural covariance between DAT-SPECT and FA values across groups.

### Prediction Models: Symptoms Severity and Disease Progression

The linear combinations of DAT-SPECT values in the putamen, and MRI differences found in the group comparison, significantly predicted the BL MDS-UPDRS2 and MDS-UPDRS total but not the other subscales (see **Tables 3**, **4**). The MD values in the operculum, the DAT-SPECT signal in the putamen and age were the most significant predictors for the MDS-UPDRS2, as summarized in **Tables 3**, **4**. The significant predictors for

TABLE 2 | Main differences for group comparisons and covariance analyses done at baseline between healthy controls (HC) and Parkinson's disease patients (PD).

FIGURE 1 | Results of group comparisons for diffusion MRI measures. (a) Increased FA values in Parkinson's disease patients compared to healthy controls at pFWE < 0.05. (b) Higher MD values for Parkinson's disease patients with respect to healthy controls at pFWE < 0.05.

the MDS-UPDRS total were the MD values in the operculum, the putamen DAT-SPECT values, and the interaction between putamen DAT-SPECT – GM volume in premotor, prefrontal cortex and insula (see **Tables 3**, **4**).

We did not find any significant correlation between the single imaging measures and the BS MDS-UPDRS scores.

For the longitudinal prediction models we found that the combination of BL DAT-SPECT and MRI measures was not able to significantly predict the 1MDS-UPDRS scores for the group of patients with and without medication (see **Tables 3**, **4**).

However the linear combination of BL imaging measures significantly predicted 1MDS-UPDRS2 for the PD no med group, and 1MDS-UPDRS1 and 2 for the PD on med (**Tables 3**, **4**). The significant regressors for the prediction of 1MDS-UPDRS2 in the PD no med group were GM volume in the premotor and prefrontal cortex, putamen DAT-SPECT, FA in the pontine tegmentum and the interaction between DAT signal in the putamen – GM volume in premotor, prefrontal cortex and insula (**Tables 3**, **4**). For the PD on med, the significant predictors of 1MDS-UPDRS1 were the MD in the pons nuclei,

TABLE 3 | Summary of the linear models explaining the MDS-UPDRS components at baseline (BL) and the MDS-UPDRS variation (1MDS-UPDRS) 1 year after the baseline.

Models for the 1MDS-UPDRS were estimated separately for the patients without medication 1 year after baseline (no med), for the patients on L-Dopa 1 year after baseline (on med) and for the patients of both groups together (on med + no med). The model predictors were the mean imaging values extracted from the following regions: IN, insula; OP, operculum; PFC, prefrontal cortex; PMC, premotor cortex; PN, pons nuclei; Put, putamen; PT, pontine tegmentum. We report the p-value for every regressor, and the determination coefficient (R2 ), adjusted R2 , f-value, and p-value for every model. Bold characters and "∗ " indicate a significant (p < 0.05) model – (p < 0.01) regressors.

TABLE 4 | Summary of the slopes (β) of the linear regressions explaining the MDS-UPDRS components at baseline (BL) and the MDS-UPDRS variation (1MDS-UPDRS) 1 year after the baseline.

Models for the 1MDS-UPDRS were estimated separately for the patients without medication 1 year after baseline (no med), for the patients on L-Dopa 1 year after baseline (on med) and for the patients of both groups together (on med + no med). The model predictors were the mean imaging values extracted from the following regions: IN, insula; OP, operculum; PFC, prefrontal cortex; PMC, premotor cortex; PN, pons nuclei; Put, putamen; PT, pontine tegmentum. Bold characters indicate a significant (p < 0.05) model – (p < 0.01) regressors.

the DAT-SPECT in the putamen, interaction DAT – MD in the pons nuclei and age. The premotor and prefrontal GM volume, putamen DAT binding, MD in the pons nuclei and the interaction DAT in putamen – MD in pons nuclei significantly predicted 1MDS-UPDRS2 for the PD on med group.

We did not find any significant correlation between the single imaging measures and the 1MDS-UPDRS scores for both the PD no med and on med groups.

### DISCUSSION

Here, we evaluated the relative and combined value of MRI and DAT-SPECT measures as biomarkers of disease severity and clinical progression in de novo PD patients. Consistent with prior literature, we identified DAT-SPECT and MRI abnormalities in patients compared to the HC (Brooks, 1998; Lee et al., 2000; Isaias et al., 2007; Gattellaro et al., 2009; Wang et al., 2011). Most importantly, we found that in combination, these imaging abnormalities could reliably predict both current clinical symptoms and their progression over time, suggesting that they could be used as prognostic biomarkers.

### Clinical Variables

Prior to perform the image comparison and the prediction analysis of the clinical scores, we statistically compared the MDS-UPDRS across groups. As expected, we found significantly higher clinical scores for the PD patients with respect to the HC. Moreover, we observed significantly higher increase of the MDS-UPDRS 1, 2, and total scores at BL and TP1, as well as 1MDS-UPDRS1 and 1MDS-UPDRS2 for the PD group on med with respect to the PD no med group. Those results are in agreement with the fact that patients on medication exhibit more advanced disease stages. The lack of significant difference in MDS-UPDRS3 between the PD on med and PD no med groups might be explained by the fact that the motor assessment is a highly variable measure, involving both patient- and raterdependent variability (Mentzel et al., 2016; Rahmim et al., 2017). The use of a longitudinal clinical assessment over 1 year might have increased this variability, confounding potential differences between the groups.

### Regional Findings

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Earlier studies have reported altered diffusion MRI measures across different brain areas in PD and associated animal models (Peran et al., 2010; Wang et al., 2011; Zhang et al., 2011, 2015; Du et al., 2012; Schwarz et al., 2013; Tan et al., 2015; Lim et al., 2016; Loane et al., 2016; Nagae et al., 2016). Consistent with those findings, we showed distinct patterns of MD alterations in the prefrontal cortex, suggesting underlying microstructure degradation that might be due to the loss of dopaminergic input into the striatum (Hornykiewicz, 1998; Mattay et al., 2002; Gattellaro et al., 2009; Zhan et al., 2012; Kim et al., 2013).

Furthermore, we found increased FA values in PD patients in midbrain regions corresponding to the location of pontine tegmentum. This region is affected by the alpha synuclein pathology at early disease stages (Braak et al., 2003; Mori et al., 2008; Venda et al., 2010), and it is associated with the pathophysiology of sleep behavior disorder frequently observed at prodromal disease stage (Iranzo et al., 2006; Burke and O'Malley, 2013). There is substantial controversy in the literature about the magnitude, directionality and neurobiological interpretation of FA changes in this region. While Wang et al. (2011) also found increased FA values in the substantia nigra, other studies reported lowered FA in PD patients in this region (Peran et al., 2010; Rolheiser et al., 2011; Du et al., 2012; Prakash et al., 2012; Zhang et al., 2016). The reason for this divergence remains unclear but might be related to the inclusion of more advanced PD patients in those studies.

### Structural Network Findings

In agreement with previous research showing 40–60% loss of striatal dopaminergic innervations from substantia nigra in de novo PD patients, we found a significantly reduced DAT-SPECT signal in the striatum (Brooks, 1998; Lee et al., 2000; Isaias et al., 2007). Our findings on significant interactions between putamen DAT uptake and MD values in the pons nuclei are in line with the known disruption of brainstem projections to the striatum. More specifically, we found that a lower striatal DAT uptake was linked to lower MD values in PD patients, which was the opposite of what we observed in HC. This finding supports the idea that structural covariance might be more sensitive to axonal and neuronal damage in the ponto-mesencephalic tegmentum, which did not show significant MD changes in direct group comparisons. The pons nuclei represent the anatomical origin of projections modulating the dopaminergic action of the substantia nigra and they are under dopaminergic inhibitory control from the ventral tegmental area, which is known to degenerate in PD (Guiard et al., 2008). According to the physiological functions attributed to the noradrenergic system, impaired functioning of pons nuclei in PD results primarily in affective disorders (Remy et al., 2005), cognitive disturbances (Javoy-Agid and Agid, 1980), sleep disorders (Boeve et al., 2007), sensory impairment (Braak et al., 2003) and autonomic dysfunction (Orskov et al., 1987).

Furthermore, in PD patients we found a decrease in putamen DAT being linked to lower GM volume values in the premotor-prefrontal cortex, while in HC a reduction in putamen DAT was associated to a bigger GM volume. While, we did not detect any significant atrophy in those cortical regions using group comparisons, these findings suggested that progressive loss of striatal DAT uptake translates into GM volume loss in premotor-prefrontal regions. This result is in line with studies showing that significant GM volume loss is predominantly observed in more advanced PD patients exhibiting cognitive deficits (Beyer et al., 2007; Melzer et al., 2012). The clinical stage arises when the disease spreads from brainstem to basal ganglia nuclei and then to cortical regions in an ascending course (Lang and Lozano, 1998; Braak et al., 2003).

### Prediction of Symptom Severity and Disease Progression

After identifying these alterations in patients compared to HC, we then evaluated their link to current disease severity and their prognostic value for future symptoms progression measured by the MDS-UPDRS subscales. We showed that the combination of the imaging measures was significantly related to the current clinical severity measured by MDS-UPDRS2 and MDS-UPDRS total. The imaging measures that better predicted the BL MDS-UPDRS2 were the MD values in the operculum and the DAT-SPECT signal in the putamen, while the MDS-UPDRS total was better predicted by the MD values in the operculum, the putamen DAT-SPECT values, and the interaction between putamen DAT-SPECT – GM volume in premotor, prefrontal cortex and insula. The absence of significant correlation between each of those imaging measures and the MDS-UPDRS2 and MDS-UPDRS total is in agreement with results previously reported literature (McGhee et al., 2013). Moreover this result highlights the fact that the combination of those imaging measures, rather than each single one, could be used as biomarker for predicting symptoms measured by MDS-UPDRS2.

The MDS-UPDRS2 is a subscale evaluating the subject's impairment in daily life activities (Goetz et al., 2008), and it is often used to evaluate symptoms severity perceived by the patient. Consequently MDS-UPDRS2 can be used to assess improvements related to pharmacological treatments or surgical therapies such as deep brain stimulation (Rodriguez-Oroz et al., 2005).

One key objective of the study was to evaluate the association between the imaging biomarkers and disease progression. As dopaminergic treatments have substantial effects on the clinical symptoms measured with MDS-UPDRS, those treatments

represent a potential confound that needs to be controlled for. Due to this reason we first performed the predictive analysis of symptoms progression using the entire PD cohort and then we employed separate models for the PD no med and on med groups. For the patients on medications we employed the MDS-UPDRS scores assessed off medication. The off medication clinical assessments performed for the PPMI study were acquired asking the patients not to take PD medication the day before. While, the half-life of L-Dopa is short enough to achieve a sufficient wash out in 24 h, the half-life of dopamine agonists or other medications could be longer and therefore be not sufficient to estimate a proper off medication symptom severity (Brooks, 2008; Onofrj et al., 2009; Reichmann, 2009). For this reason, we restricted our analyses of the patients treated only with L-Dopa.

We found that GM volume extracted from the premotor cortex, putamen DAT, the interaction between both, and brainstem FA values were significant predictors for 1MDS-UPDRS2 in the untreated patients. The 1MDS-UPDRS2 in the group on medication was significantly predicted by the same measures with the addition of MD assessed in the pons nuclei. Though the separation of PD patients in no med and on med groups was performed to exclude confounds of treatment onto the MDS-UPDRS scales, the separate analyses could be also considered as independent replication samples of the prognostic link of MRI and DAT measures onto clinical symptoms measures. This consistency of the significant association between the identified structural measures and 1MDS-UPDRS2 across both groups strengthens the confidence in these findings.

Finally, we found that the MD in the pons nuclei, putamen DAT-SPECT, interaction DAT – MD in the pons nuclei were significant predictors of 1MDS-UPDRS1, which is associated to non-motor and cognitive deficits. However, the significant prediction of the 1MDS-UPDRS1 was only observed in the on med group. Beside the possibility of being a false positive finding, this difference could also reflect the more severe phenotype in the on med group or the more complex effects of treatment onto clinical severity captured by MDS-UPDRS1. In fact the L-Dopa treatment of the on med group is likely to interfere with the clinical measures of symptoms severity as no off medication assessment was performed for the MDS-UPDRS1. Therefore it is important to replicate this association in an independent cohort in order to evaluate the prognostic ability of those imaging measures for the 1MDS-UPDRS1.

The linear combination of DAT-SPECT and MRI measures was not able to significantly predict the symptoms progression at TP1 using the data of both PD no med and on med groups. This results might be indicative of the fact that using heterogeneous groups of patients could confound the reliability of predictive analysis (Miller and O'Callaghan, 2015; Tuite, 2016).