A Vallet

N Del Campo

E O A Hoogendijk4

Lokossou

• O Balédent

Z Czosnyka

L Balardy

P Payoux

P Swider

• S Lorthois

E Schmidt

Biomechanical response of the CNS is associated with frailty in NPH-suspected patients

Keywords: Biomechanics, Frailty, Normal pressure hydrocephalus, Lumbar infusion test, Phase contrast magnetic resonance imaging, Neurodegenerative CNS changes

Frailty is known to predict dementia. However, its link with neurodegenerative alterations of the central nervous system (CNS) is not well understood at present. We investigated the association between the biomechanical response of the CNS and frailty in older adults suspected of normal pressure hydrocephalus (NPH) presenting with markers of multiple co-existing pathologies. The biomechanical response of the CNS was characterized by the CNS elastance coefficient inferred from phase contrast magnetic resonance imaging and intracranial pressure monitoring during a lumbar infusion test. Frailty was assessed with an index of health deficit accumulation. We found a significant association between the CNS elastance coefficient and frailty, with an effect size comparable to that between frailty and age, the latter being the strongest known risk factor for frailty. Results were independent of CSF dynarnics, showing that they are not specific to the NPH neuropathological condi tion. The CNS biomechanical characterization may help to understand how frailty is related to neurodegeneration and detect the shift from normal to pathological brain ageing.

Introduction

It is well established that the accumulation of neurodegen erative brain pathologies occurs years before the onset of clinical symptoms [l]. This long preclinical stage represents a unique opportunity for preventive therapeutic strategies [START_REF] Sperling | Preclinical Alzheimer disease-the challenges ahead[END_REF]. A major unresolved challenge is the identification of mechanisms that can be quantified before clinical signs become significant.

Frailty is conceived as an acceleration of the ageing pro cess leading to an increased risk of negative health-related outcomes [START_REF] Cesari | The stress of aging[END_REF] including dementia. Even if it is not brain specific, frailty bas been demonstrated to predict Alzhei mer's disease (AD) and related dementia [START_REF] Kojima | Frailty as a predictor of alzheimer disease, vascular dementia, and all dementia among community-dwelling older people: a systematic review and meta-analysis[END_REF][START_REF] Song | Age-related deficit accumulation and the risk of late-life dementia[END_REF][START_REF] Song | Nontraditional risk factors combine to predict Alzheimer disease and dementia[END_REF], and has been associated with cognitive impairments [7][8][START_REF] Rolfson | An assessment of neurocognitive speed in relation to frailty[END_REF], structural brain abnormalities [START_REF] Kant | The association between brain volume, cortical brain infarcts, and physical frailty[END_REF][START_REF] Gallucci | Associations between the Frailty Index and Brain Atrophy: the Treviso Dementia (TREDEM) Registry[END_REF], cortical brain infarcts [START_REF] Kant | The association between brain volume, cortical brain infarcts, and physical frailty[END_REF] and sub clinical levels of brain neuropathologies [START_REF] Buchman | Association of brain pathology with the progression of frailty in older adults[END_REF]. It is thought that frailty and pathological brain ageing share common underlying neurobiological processes. Quantifying these brain-specific processes rnight open a window of opportu nity for the prevention of neurodegenerative diseases.

The recent application of biomechanical models in neurodegenerative diseases has provided ground-breaking insights into the brain [START_REF] Goriely | Mechanics of the brain: perspectives, challenges, and opportunities[END_REF], related, for example, to the interstitial fluid transport in the parenchyma [START_REF] Holter | Interstitial solute transport in 3D reconstructed neuropil occurs by diffusion rather than bulk flow[END_REF] or the occlusion of capillaries in AD [START_REF] Hernández | Neutrophil adhesion in brain capillaries reduces cortical blood flow and impairs memory function in Alzheimer's disease mouse models[END_REF]. It allows the dynamic response of a system to mechanical stressors to be quantified and therefore provides information on its mechanical resilience.

The objective of the present study was to explore the potential of the biomechanical approach to better characterize the brain-specific processes shared by both brain pathological ageing and frailty. We hypothesized that the biomechanical response of the CNS could be associated with frailty in older patients presenting with neurodegenerative pathologies. This hypothesis was tested on a cohort of patients suspected of normal pressure hydrocephalus (NPH) as [START_REF] Jack | Hypothetical model of dynamic biomarkers of the Alzheimer's pathological cascade[END_REF] these patients often present with co-existing neurodegenerative diseases [START_REF] Klassen | Normal pressure hydrocephalus[END_REF][START_REF] Malm | Influence of comorbidities in idiopathic normal pressure hydrocephalusresearch and clinical care. A report of the ISHCSF task force on comorbidities in INPH[END_REF] and (2) the lumbar infusion test, which is performed as part of their clinical assessment [18], is suitable for measuring the biomechanical response of the CNS.

Methods

Study population

One hundred patients suspected of NPH (46 women and 54 men aged 52-92 years) were included in the PROLYPHIC cohort between 2013 and 2015 at Toulouse University Hospital. The suspicion of NPH was based on established guidelines [START_REF] Relkin | Diagnosing idiopathic normal-pressure hydrocephalus[END_REF], i.e. the presence of (1) clinical symptoms of gait or balance disturbance, cognitive impairment or urinary disorders, (2) neuroimaging evidence of ventriculomegaly (i.e. Evans Index > 0.3) and (3) narrow callosal angle, temporal horn enlargement or periventricular signal changes.

Standard protocol approvals, registrations and patients' consent

The study was approved by the competent authority and registered at ClinicalTrials.gov under no NCT0179864. The local ethics committee approved the study. All patients gave their written informed consent.

Clinical evaluation and experimental procedures

All patients underwent a structured clinical evaluation that included the following rating scales: Unified Parkinson's Disease Rating Scale (UPDRS), Mini Nutritional Assessment (MNA) [START_REF] Vellas | The mini nutritional assessment (MNA) and its use in grading the nutritional state of elderly patients[END_REF], modified Score of Emergency Geriatric Assessment (SEGAm) [START_REF] Feck | Screening of frailty in family practice by the modified SEGA grid[END_REF], and International Prostate Symptom Score (IPSS) [START_REF] Araki | Assessment of voiding dysfunction in Parkinson's disease by the international prostate symptom score[END_REF]. Blood and lumbar CSF samples were collected for fluid biomarker assessment. Patients received an infusion test where the intracranial pressure (ICP) was recorded during a constant rate lumbar saline injection in order to characterize their cerebrospinal fluid (CSF) dynamics [18]. A majority of patients also underwent phase contrast (PC) magnetic resonance imaging (MRI). Combined with the ICP data, PCMRI data allowed a parameter characterizing the biomechanical response of the CNS, the CNS elastance coefficient, to be assessed, thus making this cohort unique for the purpose of our study. Finally, most patients underwent [ 123 I]FP-CIT dopamine transporter (DAT) single-photon emission computed tomography (SPECT) imaging and FLAIR MRI.

In vivo markers of neurodegenerative pathologies

The presence of AD pathology was examined based on the ratio between CSF tau and amyloid beta 42 (Aβ 42 ) peptides using a cut-off of 0.215 [START_REF] Mo | A multinational study distinguishing Alzheimer's and healthy patients using cerebrospinal fluid tau/Aβ42 cutoff with concordance to amyloid positron emission tomography imaging[END_REF]. CSF levels of tau and Aβ 42 peptides were measured with standard commercial immunoassays. Presynaptic dopamine deficiency, which is a characteristic of Parkinsonian syndrome, was examined visually by an expert neurologist using [ 123 I]FP-CIT DATSPECT. The presence of white matter lesions was ascertained on FLAIR MRI images using the Fazekas scale [START_REF] Fazekas | MR signal abnormalities at 1.5 T in Alzheimer's dementia and normal aging[END_REF] with a cut-off of ≥ grade 2. Finally, abnormal CSF dynamics was quantified via the CSF outflow resistance R 0 derived from the lumbar infusion test [18] described in detail below. A threshold of R 0 >12 mmHg/mL/min was chosen as a marker of altered CSF dynamics and increased risk of NPH. Although the above in vivo markers do not, by themselves, corroborate the clinical diagnosis of neurodegenerative diseases, they do flag an increased risk of having or developing them. For each marker, the prevalence of patients at risk was calculated only in patients with available data.

Assessment of the biomechanical response of the CNS

PCMRI protocol and image analysis

The 2D PCMRI was used as a non-invasive technique to quantify intracranial blood flows as reported elsewhere [START_REF] Balédent | Cerebrospinal fluid dynamics and relation with blood flow: a magnetic resonance study with semiautomated cerebrospinal fluid segmentation[END_REF]. Patients who could not undergo PCMRI were excluded from the study. Other participants were imaged in a 3.0 T magnetic resonance (MR) scanner (Philips Achieva, Philips, Best, Netherlands) with a commercial 32-channel head coil. Retrospective cardiac-gated PCMRI sequences were performed with a plethysmograph set on the subject's finger to synchronize the acquired images with the subject's heartbeat. The selected segment was perpendicular to the intracranial internal carotids, basilar artery, straight sinus and sagittal sinus vessels. The acquisitions were made with an encoding velocity of 80 cm/s and 32 time points were sampled for each cardiac cycle. Preprocessing steps, including velocity aliasing correction, noise reduction and vessel segmentation, were performed by a person blinded to the biomechanical data and using the dedicated Flow Analysis software, version 2018-12-12 [START_REF] Balédent | Cerebrospinal fluid dynamics and relation with blood flow: a magnetic resonance study with semiautomated cerebrospinal fluid segmentation[END_REF]. Data identified by an independent reviewer as non-reliable due to poor image or segmentation quality were excluded from the study.

The instantaneous intracranial arterial flow Q a (t) was defined as the sum of blood flows in the intracranial internal carotid and basilar arteries. The instantaneous intracranial sinus flow Q s (t) was defined as the sum of blood flows in the straight and sagittal sinuses. It was assumed that during the whole cardiac cycle, the blood volume entering the cranial cavity through arteries was equal to the blood volume exiting it through veins. However, the straight and sagittal sinuses are not the only exit routes for blood. A correction factor was therefore defined to take the undetected blood volume exiting the cranial cavity into account:

corr = ∫ T 0 Q a (t)dt∕∫ T 0 Q s (t)dt
, where T is the cardiac cycle period. The total instantaneous venous flow was then estimated as

Q v (t) = corr Q s (t).
The instantaneous blood volume change was

V(t) = ∫ t 0 Q a (t)dt -∫ t 0 Q v (t)dt . The maximal blood volume change was defined as ΔV = max{V(t)} t∈[0,T] -min{V(t)} t∈[0,T] .
Figure 1 shows a typical sample of PCMRI data and the associated arterial blood flow, venous blood flow and volume measurements.

Infusion test protocol and intracranial pressure analysis

Patients were awake and lying horizontally in the left lateral recumbent position. A lumbar puncture was performed with a needle connected to an infusion pump and to a pressure transducer (Fig. 2a). The CSF pressure, once zeroed at the third ventricle level, is a surrogate marker of ICP [START_REF] Lenfeldt | CSF pressure assessed by lumbar puncture agrees with intracranial pressure[END_REF]. Fast ICP components are strongly coupled to the blood pressure and therefore have a pulsatile behaviour at the heart rate frequency. The ICP was digitized with a sampling frequency of 100 Hz and stored in a computer for offline analysis. First, the ICP was measured for 5 min at baseline, where the mean pressure was noted as P b . Then, the lumbar subarachnoid space was infused with saline solution at a rate of 1.5 mL/min. Subsequently, ICP increased until a plateau was reached where the mean pressure was noted as P max . The resistance R 0 was calculated from the ICP mean curve as R 0 = (P max -P -P b )∕I f [18], where P is an artefact due to the needle resistance, which was easily measured on the ICP curve (Fig. 2b). For patients with elevated R 0 , the test was sometimes stopped if ICP came close to the safety threshold of 40 mmHg even if the plateau had not been reached. In this case, P max was extrapolated from a sigmoid curve fit prediction.

CNS elastance coefficient assessment

At each cardiac cycle, the pulsating blood flow induces a blood volume change ΔV and an ICP change ΔP . Clini- cal observations showed that in a patient at rest with constant blood pressure and cardiac stroke volume, the change in pressure ΔP was proportional to the mean ICP [27]. The mathematical model proposed by Marmarou [START_REF] Marmarou | A nonlinear analysis of the cerebrospinal fluid system and intracranial pressure dynamics[END_REF] provides the corresponding linear relationship

ΔP = [exp(E.ΔV) -1][P d -P -P 0 ]
, where E is the CNS elastance coefficient (1/mL), P d is the local diastolic pres- sure, and P 0 is the reference pressure (mmHg). The CNS elastance coefficient characterizes the biomechanical response of the CNS and measures its ability to adapt to a change of volume (see "Discussion").

From the infusion test data and at each cardiac cycle, the ICP amplitude, ΔP , was measured as the difference between the local maximum systolic pressure and local minimum diastolic pressure, P d . A linear regression of ΔP versus P d dataset was performed to assess the slope = [exp(E.ΔV) -1] and the abscissa axis intersect P 0 (Fig. 2c). The regression used the random sample consensus algorithm [START_REF] Fischler | Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography[END_REF], which gave robust regression, insensitive to the outliers induced by measurement artefacts or patients' movements. Fits with an R2 score lower than 0.9 were considered as non-reliable measures. The CNS elastance coefficient E = ln( + 1)∕ΔV was deduced from the slope, , of the linear fit and from the blood volume change, ΔV , previously calculated from PCMRI data.

Frailty assessment

Frailty was quantified by means of a frailty index (FI) based on the deficit accumulation approach, which has been widely validated across many settings and populations [START_REF] Searle | A standard procedure for creating a frailty index[END_REF][START_REF] Rockwood | Frailty defined by deficit accumulation and geriatric medicine defined by frailty[END_REF]. The FI was defined as the ratio, ranging from 0 (not frail) to 1 (very frail), between the number of health deficits presented by the individual and the total number of deficits considered. A total of 40 variables were selected to measure the deficits in (1) executive functioning, (2) mentation, behaviour and mood, (3) gait, (4) rigidity and agility of extremities, (5) blood laboratory tests of markers related to kidney function, metabolic function and inflammatory conditions, which are of particular importance in older adults [START_REF] Vásárhelyi | Lab test findings in the elderly[END_REF], (6) urinary symptoms, (7) nutrition and (8) history including medication intake and the presence of previously diagnosed hypertension or diabetes mellitus. Table 1 presents the variables and thresholds for deficit status. Following the guidelines of Searle et al. [START_REF] Searle | A standard procedure for creating a frailty index[END_REF] for creating a frailty index, we did not include variables that had more than 5% missing data and less than 1% of participants meeting criteria for the deficit. Patients presenting more than 10% of missing data on the 40 health deficits considered were excluded.

Statistical analysis

Statistical analyses were performed using STATA version 15 (College Station, TX). Descriptive analysis of the frequency distribution of all variables was performed. To ascertain normality, continuous variables were inspected through histograms and Skewness-Kurtosis tests. Depending on the distribution of the variables, the variable characteristics were presented as mean ± SD or median with interquartile ranges (IQRs), the group differences were tested with a t-test or Wilcoxon rank-sum test and correlations were assessed by Pearson analysis or Spearman's rho analysis. The association between FI, age and the CNS elastance coefficient was further determined with a linear regression analysis. Three models were tested: one model (A) including age adjusted for sex, one model (B) where the CNS elastance coefficient was added, and a final model (C) where the CSF resistance to outflow R 0 was added as it seemed to be the prin- cipal covariate in a population suspected of NPH. For each model, regression coefficients, normalized effects (beta) and p-values were reported. Skewness and kurtosis of standardized residuals were examined to ensure that assumptions of homoscedasticity were satisfied.

Data availability

Any qualified investigator may contact the principal investigator (E. S.) with details about (1) his qualifications, (2) the resources requested and (3) the proposed research question. All requests will be reviewed for availability and scientific merit. The baseline pressure was 15 % higher (p = 0.02) and the maximum pressure P max during the infusion test was 54% higher (p < 0.001) in the group of patients with elevated CSF outflow resistance ( R 0 > 12 mmHg/mL/min). No significant differences appear on the other variables between the two groups. The linear regression was not much affected by adding the CSF outflow resistance to the model (Table 3, model C).

Results

Characteristics of the study population

Discussion

We found a significant association between the CNS elastance coefficient and frailty in a cohort of older adults suspected of NPH and presenting multiple comorbid neuropathological conditions. This association was maintained even after adjustment for CSF outflow resistance, showing that it was not primarily driven by CSF dynamics abnormalities that are characteristic of NPH. Our findings provide the first evidence that altered biomechanical response of the CNS is associated with global health deficits in older adults undergoing pathological brain ageing. Neurodegenerative diseases are usually characterized according to their distinct clinical and pathological features. However, recent evidence suggests that age-related neurodegenerative diseases are often accompanied by multiple, partly overlapping, pathological brain changes [START_REF] Irwin | Neuropathological and genetic correlates of survival and dementia onset in synucleinopathies: a retrospective analysis[END_REF][START_REF] Iturria-Medina | Early role of vascular dysregulation on late-onset Alzheimer/'s disease based on multifactorial data-driven analysis[END_REF]. In this context, the association found here between the CNS elastance coefficient and the FI can be interpreted as a characteristic of pathological ageing of the brain, which is not specific to any neurodegenerative disease in particular, for four following reasons. (1) Although the patients of our study were suspected of NPH, not all presented CSF dynamic alterations (43% had normal CSF outflow resistance and were not shunted). ( 2) The regression model of frailty remained unchanged when the CSF outflow resistance was considered. Neither the frailty nor the CNS elastance coefficient differed between patients with or without increased CSF resistance to outflow. Therefore, the results were not specific to altered CSF dynamics. This suggests the idea that factors weakly associated with neurodegenerative disorders can, when taken together, still contribute to a global condition leading to neurodegenerative diseases. This suggests that the CNS biomechanical alterations, associated with general health deficits, might set up conditions of increased vulnerability that lead to the onset and progression of neurodegenerative diseases.

Given that age-related neurodegenerative diseases are often characterized by multiple brain alterations combining lesions of several, partly overlapping, neuropathologies [START_REF] Rahimi | Prevalence of mixed pathologies in the aging brain[END_REF], it is essential to capture multi-factorial causes. It has recently been suggested that dynamic indicators of the response of physiological systems to stimuli can add value to the static FI measurements of health deficits [START_REF] Rikkert | Rerouting geriatric medicine by complementing static frailty measures with dynamic resilience indicators of recovery potential[END_REF]. The dynamic approach indeed gives additional information on the systemic resilience and the recovery potential of a patient. Altogether, the above results suggest that the CNS elastance coefficient, being a quantitative indicator of the CNS response to blood pulsatility, could be a dynamic indicator that would improve frailty assessment. More generally, the CNS biomechanical characterization may be a relevant modality, in addition to conventional clinical and imaging techniques, to identify pathological changes in the brain. It could therefore help to identify the shift from normal to pathological brain ageing as recent studies have shown the benefits of multi-modal approaches for this purpose [START_REF] Khan | An algorithm for preclinical diagnosis of alzheimer's disease[END_REF][START_REF] Payton | Combining cognitive, genetic, and structural neuroimaging markers to identify individuals with increased dementia risk[END_REF]. . This study had some limitations. First, our results relied on the assumption that the blood volume change ΔV was the same during the PCMRI examination and the infusion test, which were not performed at the same time. However, PCMRI flux measurements have been reported to be reproducible [START_REF] Spilt | Reproducibility of total cerebral blood flow measurements using phase contrast magnetic resonance imaging[END_REF], indicating that the haemodynamics remains unchanged when patients are in similar conditions. Here, the patients were lying horizontally in both examinations, had small cardiac frequency differences between the two occasions (6±5%), and were examined within a short time interval (3 ± 6 days). Second, the cross-sectional design of the study did not allow the directionality of the association between frailty and the CNS elastance coefficient to be resolved. Third, variables not considered in the present study may be responsible for the apparent relationship between frailty and CNS elastance coefficient. Noteworthy, we checked that the main confounder related to our NPHsuspected population, the resistance to CSF outflow (R0), is not such a confounder variable. Fourth, our study does not provide information on the relevance of our approach in the preclinical stage as all patients presented clinical symptoms. The absence of an age-matched control group did not allow exploring whether the CNS elastance coefficient can distinguish pathological from normal brain ageing. The invasiveness of the lumbar puncture limits such a wider applicability of our approach. A range of alternative non-invasive ICP monitoring techniques are emerging, including monitoring of the optic nerve sheath diameter or of the tympanic membrane displacement [START_REF] Khan | Noninvasive monitoring intracranial pressure: a review of available modalities[END_REF]. These techniques will surely be improved in the near future to eventually provide reliable quantification tools of biomechanical markers. This will allow the predictive value of CNS biomechanical markers for dementia to be explored using longitudinal designs in larger samples of community-dwelling older adults. Such studies are needed to resolve the directionality of the association between frailty and the CNS elastance coefficient and to explore the preclinical stage.

Conclusions

There is an urgent need to identify new quantitative markers of the shift from normal to pathological brain ageing. Preventive intervention strategies, including health-promoting life choices, are thought to be our best chance of avoiding or delaying the onset and development of neurodegenerative diseases. This study shows that the biomechanical response of the CNS is associated with the FI, which is known to predict dementia. By quantifying the CNS response to mechanical stressors such as the blood pulsatility, the biomechanical characterization of the CNS provides dynamic information that goes beyond the FI static measurement of health deficits. Hence, the CNS biomechanical characterization, as a complementary tool to the FI, may help the early detection where P 0 is neglected. This study shows that greater frailty is associated with higher CNS elastance coefficient, leading to an increased risk for excessive ICP peaks, i.e. to an increased brain vulnerability to mechanical stressors of pathological ageing of the brain and should be further investigated in longitudinal studies.

Fig. 1

 1 Fig. 1 Phase contrast MRI, typical blood flow measurements and analysis. Panel A: indication of the position of the 2D PC section on angiogram. Panel B: phase contrast angiogram used to determine acquisition location and level. Panel C: velocity-encoded MR images of the internal carotid arteries, basilar artery, straight sinus and superior sagittal sinus. Panel D: blood arterial flow Q a (t) (red curve), venous flow Q v (t) (blue curve), blood volume change V(t) (black dashed curve) and maximal blood volume change ΔV (orange arrow) extracted from the PCMRI data. The left axis shows flow values; the right axis shows volume values

Fig.

  Fig. 2 Lumbar infusion test, typical intracranial pressure measurements and analysis. Panel A: lumbar infusion test scheme: The intracranial pressure (ICP) is measured while a saline fluid is injected at a constant flow rate through a lumbar puncture. Panel B: ICP evolution during the infusion test. Blue line: ICP measurement; orange line: smoothed ICP (low-pass filter). The mean pressure at plateau, P max , and the mean pressure at baseline, P b , are indicated in black dotted lines. The pressure artefact induced by the needle, δP, the ICP amplitude, ΔP, and the diastolic pressure, P d , are indicated in red. Panel C: ICP amplitude, ΔP, versus the diastolic pressure, P d . Circles: measured values; black dashed line: linear regression. The reference pressure, P 0 , and the slope, α, are indicated in red

Fig. 4

 4 Fig. 4 CNS elastance coefficient and age relationships to the components of frailty. The relative difference of mean CNS elastance coefficients (a) and mean ages (b) in the samples with and without deficit

Fig. 6

 6 Fig. 6 Higher CNS elastance coefficient increases vulnerability to mechanical stressors. The intracranial pressure (ICP) relative change, ΔP/P, induced by an instantaneous intracranial volume change ΔV is plotted for a low and a high value of the CNS elastance coefficient (study mean value ± standard deviation). The prediction is made according to Marmarou's model, ΔP = [exp(E.ΔV) -1][P -P 0 ] ,where P 0 is neglected. This study shows that greater frailty is associated with higher CNS elastance coefficient, leading to an increased risk for excessive ICP peaks, i.e. to an increased brain vulnerability to mechanical stressors

Table 3

 3 Regression analysis of frailty Results are considered as significant for p < 0.05

	FI	Model A			Model B			Model C
	N	60			60			60
	R-squared (%)	15			23			24
	Variables	Coef.	Beta	P > |t|	Coef.	Beta	P > |t|	Coef.	Beta	P > |t|
	Elastance coef. (/mL) -	-	-	0.33*	0.3*	0.012*	0.33*	0.3*	0.012*
	Age (y)	0.0084* 0.37*	< 0.01* 0.0082*	0.36*	< 0.01*	0.0083*	0.36*	<0.01*
	Sex	-0.013	-0.046	0.71	-0.014	-0.05	0.68	-0.014	-0.048	0.68
	R 0 (mmHg/mL/min)	-	-	-	-	-	-	-0.002	-0.093	0.43

*

Acknowledgements This study was supported by a grant from the French Ministry of Health with the participation of the Groupement Interrégional de Recherche Clinique et d'Innovation Sud-Ouest Outre-Mer Hospitalier (PHRCInterRégional 2010), by research funding from the Occitania Region (RPBIO 2015 no 14054344), and by the European Research Council under the European Union's Seventh Framework Program (FP7/2007-2013)/ERC grant agreement no 615102 (https :// erc.europ a.eu/).

Author contribution AV designed and conceptualized the study, processed, analysed and interpreted the data, performed the statistical analysis, and drafted and revised the manuscript. NDC and EH designed and conceptualized the study on the frailty part, interpreted the data, and drafted and revised the manuscript. AL segmented and analysed PCMRI data. OB provided tools and methods for PCMRI acquisition and data processing and analysed PCMRI data. ZC interpreted ICP measurements and revised the manuscript. LB and PP played a major role in the acquisition of clinical data and MRI data, respectively. PS designed and conceptualized the biomechanical part of the study, interpreted the data, and drafted and revised the manuscript. SL and ES designed and conceptualized the study, interpreted the data, drafted and revised the manuscript, supervised the study and obtained funding.

Compliance with ethical standards

Conflicts of interest On behalf of all authors, the corresponding author states that there is no conflict of interest.