Translating cognitive neuroscience to fitness to drive using a neuroergonomic approach

Thesis

Reference

Translating cognitive neuroscience to fitness to drive using a neuroergonomic approach

VAUCHER, Paul

Abstract

Non-pathological or normal ageing is accompanied by brain alterations that are the result of natural changes occurring with age and our ability to compensate for them. Compared to younger adults, older adults have reduced vision, more difficulties in detecting relevant information they are not intending to and require more time to process sensorial information.

Little is known on how these changes affect behaviour in a natural environment. Relying on a translational approach at the frontiers between neurobiology, psychophysics, neuropsychology and epidemiology, we were able to: explore the needs for innovative instrumentations to detect cerebral decline in clinical settings; develop and validate a new computed neuropsychological instrument designed to measure cerebral decline in healthy older adults; explore the link between processing speed and on-road driving performance; and investigate the effects of being able to anticipate on visual processing speed.

VAUCHER, Paul. Translating cognitive neuroscience to fitness to drive using a

neuroergonomic approach. Thèse de doctorat : Univ. Genève et Lausanne, 2014, no. Neur.

133

URN : urn:nbn:ch:unige-393157

DOI : 10.13097/archive-ouverte/unige:39315

Available at:

http://archive-ouverte.unige.ch/unige:39315

Disclaimer: layout of this document may differ from the published version.

FACULTÉ DES S^CIENCES DOCTORAT EN NEUROSCIENCES

des Universités de Genève et de Lausanne

UNIVERSITÉ DE GENÈVE, faculté des sciences

Professeur Patrice MANGIN, Directeur de thèse Dr Bernard FAVRAT, Co-directeur de thèse

TRANSLATING COGNITIVE NEUROSCIENCE TO FITNESS TO DRIVE USING A NEUROERGONOMIC APPROACH

THÈSE

Présentée à la Faculté des Sciences de l’Université de Genève

pour obtenir le grade de

Docteur en Neurosciences

par

Paul VAUCHER

Originaire de Val-de-Travers

Thèse N° 133

Editeur ou imprimeur : Université de Genève 2014

Date de creation de l’imprimatur: 14 juillet 2014 Thèse numéro: 133

Imprimeur: Université de Genève, Genève, Suisse Cette thèse a donné lieu à des publications:

Paul Vaucher, Isabel Cardoso, Janet L. Veldstra, Daniela Herzig, Michael Herzog, Patrice Mangin and Bernard Favrat. A Neuropsychological Instrument Measuring Age-Related Cerebral Decline in Older Drivers:

Development, Reliability, and Validity of MedDrive. Frontiers in Human Neuroscience: 2014 (under review 3^rd version)

Paul Vaucher, Cyndia Di Biase, Emma Lobsiger, Isabel Margot Cattin, Bernard Favrat, Ann-Helen Patomella.

Reliability of P-Drive in occupational therapy following a short training; a promising instrument measuring seniors’ on-road driving competencies. British Journal of Occupational Therapy: 2014 (Accepted for publication) Paul Vaucher, Daniela Herzig, Isabel Cardoso, Michael Herzog, Patrice Mangin, Bernard Favrat. The Trail Making Test as a Screening Instrument for Driving Performance in Older Drivers; A Translational Research. BMC Geriatrics : 2014 (under review)

Emma Wallace, Susan M. Smith, Rafael Perera-Salazar, Paul Vaucher, Colin McCowan, Gary Collins, Jan Verbakel, Monica Lakhanpaul, Tom Fahey. Framework for the impact analysis and implementation of Clinical Prediction Rules (CPRs). BMC Medical Informatics and Decision Making. 2011;11:62

II.. C C

“Do not try to live forever, you will not succeed.”

George Bernard Shaw (1856-1950)

I. CONTENTS 5

II. ACKNOWLEDGMENTS 9

III. ABSTRACT 13

IV. R^{ÉSUMÉ EN} F^RANÇAIS 17

V. INTRODUCTION 21

1. Thesis’ framework 23

2. Causation across disciplines in traffic safety research 39

3. Cerebral decline and ageing 51

4. Visual processing, ageing and driving 83

5. Attention and ageing 105

VI. SUMMARY OF RESULTS 125

1. Articles submitted for publication 127

1.1 MedDrive 127

1.2 P-drive 127

1.3 Trail making test 128

1.4 Framework for the implementation of CPRs 128 2. Analysed data for future publications 129 2.1 Temporal visual processing loss with ageing 129 2.2 Compensation by goal-oriented control 134 2.3 Instruments for investigating fitness to drive 138

VII. D^ISCUSSION 143

1. Results in light of previous published research 145

2. Limitations 152

3. Practical implications 157

4. Future perspectives 158

5. Conclusion 159

6. References 160

VIII. ARTICLES 171

1. Development and validity of MedDrive 173

2. Reliability of P-drive 225

3. Validity of the Trail Making Task 251

4. Implementation of clinical prediction rules 277

IIII.. AA

“Are you thankful for not being young?’

Yes, sir. If I was young, it would all have to be gone through again, and the end would be a weary way off, don’t you see?”

Charles Dickens (1812-1870), in Our Mutual Friend

I feel like I can see home appearing on the horizon after a long journey at sea. My thoughts are naturally for Cloé and Téo, my wife and son, who were my shining northern star, always guiding me safely through my oceans of doubts, fatigue and bad moods, and for too many evenings were waiting for me to come to dock.

My gratitude then drifts to my mentor and friend, Bernard Favrat. He is the mast of my ship, the sextant for my navigation skills, someone who has trusted me right from the start and has encouraged me to cherish what I now value most in research: curiosity, humility, openness, rigour, respect, humanism.

Strangely enough, a light breeze has me escaping in a comfortable fog in which time stands still. It is to embrace my grandmother I love so dearly and who ended up having a car that had more plastic than metal from all the collisions she had whilst driving it. She let me love, appreciate and admire her, with her eyes that sparkled right up to her last days, regardless of the devastating floods that seemed to wash away all her memories. She helped me realise how important it is to keep rejoicing even in the most difficult times.

Finally, my trip that I initially thought to be a fast, round-the-world trip, ended up being much more like a journey from “Treasure Island”. Each study participant I connected with handed me over a precious gem – an intimate part of what they were living – and in the end, what they had offered was a treasure for me. This treasure is one of the most precious gifts anyone could ever give me – to be free from the fear of growing old.

IIIIII.. AA

“My grandmother started walking five miles a day when she was sixty. She’s ninety-seven now, and we don’t know where the heck she is.”

Ellen DeGeneres

Introduction: By 2075, over 50% of the population in Europe will be aged over 60 years with a three fold increase of those aged 80 years or more compared to now. Our society needs to anticipate this demographic change and help older adults conserve their autonomy for as long as possible. Non-pathological or normal ageing is accompanied by brain alterations that are the result of natural changes occurring with age and our ability to compensate for them. Compared to younger adults, older adults have improved regulation of emotions, better life-satisfaction and are less diverted by unimportant distractors. On the other hand, they have reduced vision, more difficulties in detecting relevant information they are not intending to and require more time to process sensorial information. Little is known on how these changes affect behaviour in a natural environment. One such behaviour of interest is that of driving a car – a standardised complex task requiring both lower and higher order cognitive processing skills, for which most people are already trained. Relying on a translational approach at the frontiers between neurobiology, psychophysics, neuropsychology and epidemiology, we were able to: explore the needs for innovative instrumentations to detect cerebral decline in clinical settings; develop and validate a new computed neuropsychological instrument designed to measure cerebral decline in healthy older adults; explore the link between processing speed and on-road driving performance; and investigate the effects of being able to anticipate on visual processing speed.

Methods: Six studies were conducted for the purpose of the aforementioned objectives. The first study was a qualitative analysis of collected opinions from 15 interviewed people on the need for, and expectations of, an instrument to assess fitness to drive. The second study was derived from the summary measure of the instrument we developed for cerebral decline after collecting data from 106 older drivers (aged ≥70 years) attending a driving refresher course organised by the Swiss Automobile Association. The third study recruited 182 older drivers to test the association between cerebral decline and on-road driving performance. The fourth study measured the instrument’s reliability by having 17 healthy young volunteers repeating all tests included in the instrument five times. The fifth study explored the link between underlying psychophysical visual functions and on-road performance for 52 older drivers. The sixth study tested the modifying effects of workload and increased goal-oriented control on how alcohol affects processing speed and performance on a driving simulator. Twenty healthy, young adults were tested in a randomised, double-blinded, placebo, crossover, dose-response, validation trial including 20 healthy young volunteers.

Results: The developed instrument, called MedDrive, revealed good psychometric properties related to processing speed. It was reliable (ICC=0.853, n=17) and showed reasonable association to driving performance (R²=0.053, n=182) and responded to blood alcohol concentrations of 0.5 g/L (p=0.008). MedDrive’s composite score was mostly associated to visual search. Laboratory measures (n=52) revealed weak associations (R²<0.071 & p>0.05) between on-road driving performance and psychophysical measures including visual acuity, contrast sensitivity, visual search, motion direction detection, orientation discrimination and masking effect during the vernier test. We also observed a similar low magnitude of associations for a commonly used paper-and-pencil test, the trail making test, for both part A (R²=0.038, p=0.006) and part B (R²=0.061, p<0.001). Increased goal-oriented control significantly reduced visual processing speed (-12.4 ms; CI95% -23.5 to -1.2;

p=0.030). However, this effect was independent of blood alcohol concentration. In other words, anticipation did not compensate for increased loss due to the effects of alcohol (df=3, F=0.36, p=0.799).

Discussion: Our model for measuring cerebral decline ended up including all the measures that had previously been identified as being related to normal ageing – those linked to attention shift, dual tasking, managing distractors and noticing changes. This study therefore confirms that normal ageing is accompanied by major brain changes that affect processing speed. These changes nevertheless do not necessarily affect driving performance as they are compensated for at both neural and behavioural levels. For clinicians, there is no way of perceiving to what extent advanced cerebral decline truly affects driving competency without empirically assessing driving itself.

Occupational therapists seem to be best placed to answer physicians’ and patients’

needs for assessing fitness to drive and maintaining patients’ mobility. Our work will hopefully contribute to shifting the aims of normal ageing research from trying to explain causes of cerebral decline to exploring mechanisms that will help maintain or recover cognitive functions that older adults need to maintain an active lifestyle.

IIVV.. R R

“Au lieu de vous plaindre que les roses ont des épines, réjouissez-vous que les épines aient des roses.”

Jean-Baptiste Alphonse Karr (1808-1890)

Introduction: En Europe, d’ici 2075, plus de la moitié de la population aura plus de 60 ans et la proportion de ceux ayant plus de 80 ans aura triplé par rapport à maintenant. Notre société doit anticiper ce changement démographique et trouver des solutions pour permettre aux personnes âgées de conserver leur autonomie aussi longtemps que possible. Une de nos préoccupations est le déclin de certaines fonctions cérébrales dû à l’âge, mais non lié à des maladies. Ce déclin est provoqué par des phénomènes neurobiologiques liés à l’âge dont les conséquences dépendent de l’aptitude de chacun à les compenser. Par rapport aux jeunes adultes, les adultes plus âgés ont une meilleure capacité à réguler leurs émotions, sont globalement plus satisfaits de leur vie et sont moins enclins à être distraits par des éléments perturbateurs n’ayant pas d’importance. En contre partie, ils ont une moins bonne vision, ils ont d’avantage de peine à détecter des informations utiles auxquelles ils ne portent pas attention et ont besoin de plus de temps pour traiter les informations sensorielles. On connaît cependant mal quels effets peuvent avoir ces changements sur notre comportement dans un milieu naturel. Un comportement commun est celui de la conduite automobile – une tâche standardisée complexe pour laquelle la plupart des personnes sont déjà entraînée et qui sollicite l’ensemble des fonctions cognitives qui sont justement le plus souvent affectées par le vieillissement. En s’appuyant sur une approche transversale à la frontière de la neurobiologie, de la psychophysique, de la neuropsychologie et de l’épidémiologie, cette thèse vise à explorer les besoins cliniques d’un instrument de mesure pour évaluer l’aptitude à la conduite, développer et valider un nouvel instrument capable de mesurer le déclin cérébral lié au vieillissement normal, d’explorer les liens entre la vitesse de traitement de l’information et les performances de conduite, et d’observer l’effet de l’anticipation sur la vitesse de traitement de l’information visuelle.

Méthodes: Pour répondre à ces objectifs, nous avons menés six études. La première était une étude qualitative durant laquelle nous avons interrogé 5 experts en conduite, 5 médecins généralistes, et 5 conducteurs de 70 ans ou plus afin d’identifier les besoins et attentes pour un instrument permettant d’évaluer l’aptitude à la conduite.

La deuxième étude visait à dériver la méthode de mesure pour un instrument mesurant le déclin cérébral. Pour cela nous avons interrogé et testé 106 conducteurs âgés (≥ 70 ans) dans le cadre d’un cours de remise à jour des compétences de conduite organisé par le Touring Club Suisse, section Vaud. La troisième étude visait à évaluer l’importance de l’association entre le déclin cérébral et la performance de conduite sur route chez 182 conducteurs âgés. La quatrième étude a permis de mesurer la fiabilité de notre instrument sur 17 jeunes participants en bonne santé, testés cinq fois, à raison d’une fois par jour,. La cinquième étude consistait à tester 52 conducteurs âgés au laboratoire de psychophysique pour mesurer leurs fonctions visuelles et corréler celles-ci à leur performance de conduite sur route. La sixième étude était une étude randomisée placebo croisée à double aveugle incluant 20 jeunes volontaires dont nous avons testé la vitesse de traitement de l’information visuelle et la performance de conduite sur simulateur avec quatre taux d’alcoolémie (0 g/L, 0.5 g/L, 0.65 g/L, 0.8 g/L). Cette dernière étude nous a permis de tester l’effet modulateur de l’augmentation de la charge de travail et de l’anticipation sur le lien entre l’alcool et les performances de conduite ou le traitement de l’information visuelle.

Résultats: L’instrument que nous avons développé, appelé MedDrive, a révélé des bonnes propriétés psychométriques en rapport avec la vitesse de traitement de l’information. L’instrument s’est montré fiable (ICC=0.853, n=17) et ses mesures sont

associées à la performance de conduite sur route (R²=0.053, n=182). L’instrument s’est montré parfaitement capable de mesurer les effets de l’alcool même pour une alcoolémie de 0.5 g/L (p=0.008). Finalement, le score composite de MedDrive a montré une forte association avec la fonction de recherche visuelle. L’association entre les mesures psychophysiques et la performance de conduite sur route étaient (n=52) cependant très faibles voir inexistante (R²<0.071 & p>0.05) notamment pour l’acuité visuelle, la sensibilité au contraste, la recherche visuelle, la détection de direction de mouvement, la discrimination d’orientation, et l’effet de masque lors de l’épreuve vernier. Nous avons également observé une très faible association pour un test papier-crayon fréquemment utilisé pour évaluer l’aptitude à la conduite, le « trail making test » (partie A : R²=0.038, p=0.006 ; partie B : R²=0.061, p<0.001).

Permettre d’anticiper d’avantage la nature de la tâche à accomplir a permis de réduire le temps de traitement de l’information visuelle (-12.4 ms; CI95% -23.5 to -1.2;

p=0.030), mais cet effet était indépendant du taux d’alcoolémie. En d’autre terme, l’anticipation ne permet pas de compenser la perte de performance due à l’alcool (df=3, F=0.36, p=0.799).

Discussion : Le modèle que nous avons établi pour mesurer le déclin cérébral ne comporte que des mesures ayant été préalablement déjà identifiées comme étant associées au vieillissement, à savoir les mesures liées au transfère d’attention, à la double tâche, à la gestion de perturbateurs, et à l’aptitude de détecter des changements. Nos résultats confirment qu’avec l’âge, le cerveau humain subit des changements important qui affectent la vitesse de traitement de l’information. Ces changements n’ont cependant que peu de répercutions sur la performance de conduite.

Ceci probablement dû aux temps dont a disposé le cerveau pour s’adapter et adopter des compensations sur un plan neuronal et comportemental. En conséquence, pour les cliniciens, il est difficile de connaître l’importance des répercutions de la perte d’une fonction cognitive sur l’aptitude à la conduite sans tester la conduite. Les ergothérapeutes semblent être particulièrement bien placés pour répondre aux besoins des patients pour mieux évaluer leurs risques, et les orienter vers des solutions leur permettant de conserver leur mobilité. Cette thèse est donc un appel à réorienter la recherche dans ce domaine. Plutôt que de chercher la cause du « déclin cérébral », il semblerait plus utile d’explorer les mécanismes permettant de maintenir ou de récupérer les fonction cognitives nécessaires au maintient d’une vie active.

VV.. II

“The elderly don't drive that badly; they're just the only ones with time to do the speed limit.”

Jason Love

PART  1  –  Thesis’  framework  

P

1

T HESIS ’ FRAMEWORK

Summary

In neuroscience, the bridging of knowledge across disciplines provides not only the opportunity to improve our understanding of complex brain mechanisms, but also it makes it possible to translate such knowledge into applications that make a real difference in many people’s lives.

From this standpoint, this thesis explores:

cerebral decline in normal ageing

advances in computational science applied to cognitive neuroscience computerised means to investigate this phenomenon in clinical settings possible relationships as between these measures and driving behaviours.

This part of the introduction explains the field of research we are working on and outlines the rest of the introduction’s structure, which will be organised in five additional independent parts:

PART 2: Causation across disciplines in traffic safety research p. 39

PART 3: Cerebral decline and ageing p. 51

PART 4: Visual processing, ageing and driving p. 83

PART 5: Attention and ageing p. 105

Keywords: Translational research, consilience, neuroergonomics, modelling behaviour

1.1 Neuroergonomics and traffic safety research 1.1.1 Translational research

Since the turn of the century, neuroscience has matured in a similar way to a child moving into adulthood. In an effort to answer societal needs, huge collaborative projects have emerged to link knowledge from different fields and provide innovating instruments, designed to overcome existing barriers between fundamental research and their clinical applications. The Human Brain Project, the Brain Activity Map and the BRAIN initiative are bound to initiate important changes in the way the world will perceive neuroscience and its contributions to people’s daily lives.¹ There is an apparent dilution of pre-existing borders between basic and applied neuroscience.

Efforts to tackle problems and find solutions are present as a continuum from neurobiology to clinical neuroscience. We are most probably witnessing the beginning of the era of translational neuroscience.²

1.1.2 Consilience

In our efforts to link neurobiology to cognitive neuroscience, converging evidence from unrelated sources from different fields can sometimes lead to important and reliable conclusions. This is known as consilience and should be a milestone for anchoring coherent theories at a psychological level through disciplines.³ Solari and Stoner’s recent work on cognition is a perfect example of how consilience can be used to link evidence from neurochemistry, histology and neuroanatomy to model different cognitive processes.⁴ There is still, nevertheless, an important need to consolidate existing psychological models for modern science, so as to rely on them when developing practical applications.³ The advances in computational science are amongst the most promising leads in achieving this goal and have led to the emergence of a new field called neuroergonomics.

1.1.3 Neuroergonomics

Neuroergonomics is a field of research that matches acquired or emerging knowledge on subjects from brain functioning to new technologies, with the specific goal of overcoming people’s limitations and helping them interact with their environment in a safer and more efficient way.⁵

To achieve this we study and model neural network activities, explaining ecological functional behaviour patterns.⁶ In other words, neuroergonomics links neuroscience to human factors and neurobiology to human psychology. Simply by observing behavioural responses, it is then possible to hypothesise, model and test underlying brain mechanisms and to use these models for predicting behaviours before they occur. This approach is called computerised neuroergonomics.

The five neuroergonomic criteria for models to be valid using this approach are:

to be theoretically sound on a neurobiological point of view to be mathematically possible

to be computational to be comprehensive to be application-relevant.

Given the recent social and technological advances in personalised man-computer interactions, computerised neuroergonomics are believed to be the most promising

PART  1  –  Thesis’  framework  

fields of research to develop and improve man-machine interactions.⁷

An example of recent achievements in the field is the modelling of multiple tasking, using queuing network architecture (QNM).^{8, 9} This approach simulates brain functions as parallel processing units working in a network, but with each unit being able to perform only one task at a time (Figure 1).

Studies have also shown stimulus, onset, asynchrony and psychological refractory periods to increase with age and explain behavioural changes in driving performance.^{10, 11} For simple simulator tasks, the QNM is a solid model to work from in developing instruments and devices designed to assess and assist human driving performances.¹² Nevertheless, QNM lacks units or regulating filters to represent the top-down effects on perceptual sub-networks related to goals or expectations, emotional level regulators (i.e. sensation seeking), or modified driving behaviours related to compensation strategies. It would therefore seem important to also investigate, at an ecological level, if these factors play a role in ageing drivers.

Figure 1: Queuing network architecture model (QNM) representing principle brain neural networks utilised to perform multitasks such as driving. Modified from Liu, Feyen and Tsimhoni (2006)⁹

1.2 From neural networks to cognition

Recent convergence of computational science and neuroscience has opened new perspectives in understanding brain function in all its complexity. Park and Friston¹³ recently published a review of structural and functional neural networks and the actual state of knowledge in modelling cognition. This theme is of major importance to map the human brain¹⁴ and integrate functional connectivity to its structural neural

components. Relying on computational neuroscience, topological network analysis and biophysics of neural connection, it has now become possible to present a theoretical model of topological hierarchical modular networks explaining how diverse functions can emerge from underlying local apparent static neuronal networks. These advances are a giant leap in our ability to ground psychological theories at a neural level.¹⁵

Instead of breaking the brain down into segregated functional specialised regions, it is also possible to consider cognition as the result of global integration of multiple local integrators. Indeed, the structural organisation of the brain is both modular and hierarchical (Figure 2). Local integration is organised within a restraint neural network called a module (short-range connection), itself being constituted of submodules, whereas global integration leading to higher cognition relies on longer- range connections between modules. Each module has its own specific computational objective that can be accessed by more than one network. Therefore, the same structural network can serve more than one function. This theoretical approach of the brain, inspired by graph theory,¹⁶ is largely supported by advances in connectivity studies¹⁷ and has been shown to be reliable in identifying structural brain connectivity.¹⁸

The following two subsections will present and discuss advances in computational neuroscience, supporting the module’s hierarchical organisation of neural networks and its ability to explain cognition. The major limitation in this field of research is the difficulty of obtaining local physiological measurements for human brains. The first section explains how brain areas can be defined in humans by relying on nodes, edges, modules and hubs.

The second section discusses limitation of the structure-function connectivity convergence, before presenting a theoretical model of canonical neuronal circuits capable of integrating these divergences.

1.2.1 Nodes and edges

Brain areas are delimited by underlying structural and functional properties that are defined through architectonics, inter-areal connectivity, physiological characteristics and topological organisation.¹⁹ In humans, neuroimaging can be used to help define brain regions and recognise patterns of connectivity called nodes. Two main methods are used:

clustering

transition in connectivity.

Clustering defines nodes by building spheres around “hot spots” from large cumulated collections of task-evoked functional magnetic resonance imaging (fMRI)

Figure  2:  Multiscale   hierarchical   organization  of   brain  networks.  

Image  drawn  from   Park  &  Friston   (2013)¹⁴    

PART  1  –  Thesis’  framework  

studies. Transition in connectivity delimits nodes by identifying boundaries revealed by abrupt changes in patterns of connectivity, mainly using resting-state functional connectivity magnetic resonance imaging (rs-fcMRI), transcranial magnetic stimulation (TMS) and diffusion tensor imaging (DTI) tractography.²⁰

Once these nodes have been delimited, the difficulty is to correctly model the way they are connected one to another (i.e. define edges). Three methods are concurrently used to define edges: structural, functional and effective connectivity.

Structural connectivity using DTI has largely improved our mapping of large long- distance connections between brain regions.²¹ However, contrarily to tracing methods, DTI cannot identify short or intrinsic connections. Functional connectivity using fMRI, rs-fcMRI, electroencephalography (EEG) and magnetoencephalography (MEG) analyses the coherence between signals from different regions and creates edges of different strength between them. This approach relies on statistical dependency that can be confounded by indirect pathways. Therefore, edges identified through functional connectivity might not be anatomically connected and need to rely on structural connectivity to assure physiological plausibility. Both functional and structural connectivity cannot define the direction of the signals between nodes. To achieve this, a third method called effective connectivity is used. This approach consists of constructing a neuronal integration model and testing and adjusting it to fit at best-observed responses (fMRI, EEG, TMS).

1.2.2 Modules and hubs

Other than respecting physiological principles, neural network modelling assumes the system is organised to optimise information passing, robustness, adaptability and resilience – and all this for differing functionalities within the same structure. To achieve this, the structural architecture of the brain seems to favour multiple, short to average length paths between clustered node pairs forming modules. Modules are then connected one to another through sparse, weak extrinsic connections (Figure 3).

Some nodes (rich-club hubs) are therefore heavily connected to other nodes within the same module (provincial hubs), whereas others connect the module to other modules (connector hubs).²²

Figure   3:   Network   attributes.   A)   Local   networks   called   nodes   are   connected  to  each  other  by  edges.  B)   Some   nodes   show   a   high   degree   of   connectivity   to   other   nodes   where   others  show  a  lower  degree.  C)  Nodes   have   important   connections   in   close   range   clusters   called   modules.   Image   from  van  der  Heuvel  (2013).²³  

1.2.3 Structure-function convergences and divergences

Even if rs-fcMRI provides more information on short-range intra-cortical interconnectivity, the presence of intrinsic connectivity networks (ICNs) during resting state reveals similar network modules to those found during task-related fMRI studies (salience network, dorsal attention network, executive control network, sensorimotor visual and auditory networks).²³ Furthermore, both these approaches reveal a modular hierarchical organisation of brain networks consistent with brain anatomy, thereby supporting structural-functional convergeance.²⁴ Functional networks, however, are task dependant. Long-range network architectures are therefore believed to organise themselves through rich-club hubs, depending upon demands. This means that for any one given structural connectivity pattern, many overlaying functional connectivity patterns can co-exist.

1.2.4 The underlying mechanisms of predictive coding

Within each local network or node, neurones are organised within cortical layers and integrate excitatory and inhibitory populations of cells. Neuronal activity (excitatory postsynaptic potential, inhibitory postsynaptic potential, spiking, inter-layer synchronicity) within a single cortical column can be modelled using canonical circuits of intrinsic connections.

In predictive coding, the Bayesian brain hypothesis has us assume the brain needs to construct a realistic model of our ever-changing environment. It then updates and optimises this model, using sensorial inputs. In other words, the central neural system can be seen as a computational inference machine that is designed to actively predict and provide a meaning to sensorial inputs.¹⁵ Neuronal networks then form a probabilistic generative model of incoming sensorial signals. In this context, Friston relies on the free-energy principle to provide a predictive coding model that explains how edge strength between modules can be modulated.²⁵

By integrating intrinsic excitatory and inhibitory connectivity and extrinsic bottom-up (upwards) or top-down (downwards) connectivity to canonical microcircuits, it is possible to build a modular hierarchical neural network capable of optimising efficiency by minimising the error between the prediction and the sensorial input at each hierarchical level of processing (Figure 4).^{25, 26} In this system, oscillation and inter-network communication can be perceived as modulators of error.

Figure   4:   Modular   hierarchical   neural   network   modelling   minimisation   of   error   between   backward   prediction   and   forward   sensorial   input.   Grey   arrows   represent   forward   driven   prediction   of   error   whereas   black   arrows   represent   nonlinear   backward   connections   of   constructed   predictions   minimising   the   magnitude   of   prediction   error   (expectations).   Triangles   represent   superficial   (grey)   and   deep   (black)   pyramidal   cells.   Upper   equations   describe   the   formation   of   prediction   error;   lower   equations   in   the   black   box   represent   recognition   dynamics.  

Image  from  Friston  (2010).²⁶  

PART  1  –  Thesis’  framework  

1.2.5 Future perspectives for predictive coding

The Bayesian brain and the free-energy principle²⁵ provide a theoretical framework capable of coherently modelling neural networks on higher cognitive functions. Even if it is believed to be the most advanced model in the field, it nevertheless remains a theoretical model that does not yet dispose of enough solid proof at a physiological level, to be recognised as an existing mechanism.

We can nevertheless admit that this modular hierarchical model is an important advance upon common linear models of neural networks. This model also reveals how important it is to constantly update existing models with new acquired knowledge on neural network architecture and its functional integration. New tracing methods,²⁷ improvements in causal models for connectivity using EEG and MEG (Grangier causality or dynamic causal modelling)^{28, 29} and higher resolution in functional imaging³⁰ are bound to provide precious insights in underlying mechanisms of cognition. In this context, computational modelling and effective connectivity will undeniably have an important role and serve to bridge existing gaps between structure and function.

1.3 Ageing and driving difficulties 1.3.1 Road fatalities, accidents and ageing

In Switzerland, in 2009, 8.0% of the population was 75 years of age or older. During the same year, 316 of the 4,458 (7.1%) accidents that occurred with a severely injured road user concerned one of them. However, 63 of the 327 (19.3%) fatal accidents involved a senior and more than half of them were pedestrians.³¹

Epidemiological studies have shown that the relationship between age and road fatalities takes a U shape (Figure 5).³² Observations from other countries have confirmed that older drivers are much more likely to be involved in a fatal accident than drivers aged 40 and above.^33-35 Keall and Frith³⁶ showed that drivers of 80 years or more who fail an on-road driving test had an increased risk by 1.7 times (CI95%

1.3 to 2.2) of been involved in a crash with injury in the following two years compared to other aged drivers.

However, when comparing fatalities occurring to occupants of other vehicles (Figure 6), we notice that this difference is mainly due to their own vulnerability.³⁵ In other words, senior drivers are much more likely to die from injuries than younger drivers and increased risk of fatalities to others is not negligible, but remains very moderate. After the age of 85, drivers showed an 80% increase in liability for injury compared to drivers aged 30 to 59, but still remained less at risk than young drivers under 25 years of age.³⁵

Figure 5: Age and fatalities for Swiss drivers from 1992 to 2008. Modified from the Federal Office of Statistic report.³²

Figure 6: Age and fatalities for US drivers from 1993 to 1997. Modified from Braver & al. ³⁵

Circumstances of accidents nevertheless differ with age. The older we get, the more likely accidents are to occur in daylight hours and following turning manoeuvres, thereby provoking angled crashes.³⁷ With age, traffic violation becomes less frequent but lapses increase^38-40 especially in lane maintenance, yielding, gap acceptance,³⁹ and over- cautiousness.⁴¹

1.3.2 Consequences of driving cessation

Having physicians warn authorities about unfitness to drive prevents two patients in each thousand every year from injuring themselves while driving (4.76‰ vs. 2.73‰;

RR=1.45, CI95% 1.36 to 1.52).⁴² This however also leads to the suspension of a driver’s license for 10–30% of them and has no consequence in respect of the overall hospital admission for road related injuries.⁴³ As we would already expect a reduction of 20% of injured drivers by randomly suspending drivers’ licenses, the true benefit of the warning system is small compared to its negative consequences.

Indeed, the same study also revealed that warning authorities would lead to four additional admissions to emergency departments for depression during the same period (RR=1.27; CI95% 1.17-1.37).⁴² This is confirmed by another study that also showed increased depression and out-of-home activity for those who ceased driving.^{44, 45} However, if drivers cease willingly and have some control over this decision, the association with depression almost ceases to exist.⁴⁶ Therefore, driving cessation is something that needs to be prepared for,⁴⁷ and means have to be put in place to help ageing drivers to continue driving safely for as long as possible and also to decide when to stop.⁴⁸ Primary care physicians play an important incentive role. In Austria, 93.8% of patients with dementia ceased driving following their physicians’

recommendations.⁴⁹ In California, 87.8% of drivers cease driving for other reasons than license renewal problems.⁵⁰

1.3.3 Strategic and tactical compensations

Self-regulation has been a means of compensating for reduced driving performances in specific situations, by avoiding circumstances under which difficulties appear.⁵¹ Even if reasons for driving avoidance are often more related to lifestyle than self- regulation, strategic and tactical compensations play an important role in improving road safety and are not yet accounted for in computed neuroergonomics models.^52-55 In California, the number one reason provided by drivers for restricting their driving is poor eyesight and is evoked by 35% of drivers aged 75 years or more.⁵⁶ Other important given reasons are “not having a reason to drive anymore” (≈22%) and

“feeling concerned about having an accident” (≈17%). Observed compensations were often related to lack of confidence in a given situation and not necessarily to poorer driving performance in all difficult driving situations.^{50, 57} They were also related to driving anxiety and low confidence rather than to ageing itself.⁵⁸ Cerebral decline could also play a role, as drivers with increased spatio-visual-attention difficulties were more likely to adopt these strategies in a near future.⁵⁹ Furthermore, recognising

PART  1  –  Thesis’  framework  

difficulties and adopting behaviours to compensate for them has been shown to be associated with a reduction in accidents, compared to those who do not adopt compensations.⁶⁰

All these observations suggest that drivers naturally tend to adapt their behaviour to maintain a feeling of safety when driving. When exploring underlying mechanisms for such behaviours, Ranney,⁶¹ like Michon,⁶² distinguished taxonomic from functional models of driving behaviour, the latter including motivational models and formation-processing models. The following paragraphs will present some of these functional models.

In his risk homeostasis theory,⁶³ Wilde explains this phenomenon by suggesting drivers were willing to accept a certain level of risk and would then adapt their driving behaviour to meet this risk (Figure 7).

The Homeostasis Risk Theory, however, remains controversial.^{64, 65} It is indeed unlikely that human decisional processes rely solely on statistical risk concepts.^{66, 67} Wilde’s theory therefore lacks theoretical grounding at a neural level.

Fuller revised Wilde’s theory and suggested drivers maintained a level of difficulty instead of a level of risk.⁶⁸ This provided a solid base to explain adaptations at a manoeuvring level, but it does not account for other reasons at a strategic level, such as time constraints (Figure 8).⁶² The model was therefore adapted to include both concepts of risk and difficulty (the Difficulty Homeostasis and Risk Allostasis Theory).⁶⁹ This model nevertheless also presumes drivers are constantly monitoring their task difficulty and its associated level of risk.⁷⁰

The role of strategic and tactical compensations remains unclear. Metacognition, negative affective attitudes, sensation expectations and past experiences probably play a role. These need to be accounted for when exploring links between cognitive

Figure 7: Wilde’s Risk Homeostasis Theory (RHT), image from Wilde 1982⁵⁹  

Figure 8: Michon and Janssen’s hierarchical structure of the road user task.

Image from Michon 1985⁵⁸  

functions and ecological driving difficulties.

1.3.4 Call for further research in traffic safety

Given the health consequences of road accidents, traffic safety research has its place in biomedical research.⁷¹ Researchers in this field now need to construct sound theoretical foundations to help develop and implement sound driving safety policies.⁷² 1.4 Theoretical groundings – from neurobiology to epidemiology

Many of us remember discovering the complexity of our own body by turning transparent superimposed pages, making it possible to explore our anatomy from the skin to the bones. This introduction is organised in a similar way. Each chapter is designed to explore a different layer within existing empirical work and holds by itself. But to obtain the general view, it requires looking through all layers and recognising the shape of the domain sculpted by transportation science, epidemiology, geriatric medicine, neuropsychology, behavioural and cognitive science and neurobiology.

As a reader, you are therefore welcome to move freely through the following chapters in your order of preference and discover the following “layers”: causation across disciplines, cerebral decline, visual processing, and attention.

1.5 Aim of the thesis

Promoting an active lifestyle and maintaining mobility are the most promising leads to prevent the effects of cerebral decline on quality of life. Decisions concerning driving cessation should be made only when the underlying risks of accidents with injury is sufficiently important. Individual differences in neurobiological brain alteration with age, cognitive reserve, and the ability to put up compensatory mechanism make it impossible to provide a clear age limit from which this decision ought to be taken. To improve clinical assessment of fitness to drive, there is an urgent need to explore links between reduced cognitive performance in specific tasks and on-road driving behaviour. Focusing on visual processing, which is often affected at an early stage of cerebral decline, this thesis aims to investigate normal cerebral decline with aging and its relationship to driving behaviour.

The specific objectives of this thesis are to:

identify needs and expectations for instruments of assessing fitness to drive, at a cognitive level, in primary care settings

explore the association of the TMT to on-road driving performance

develop and validate a new computerised neuropsychological test battery to investigate cognitive functions related to driving

explore the link between primary cortical processing alteration due to normal ageing and its association to driving strategic or tactical compensations

explore psychophysical components of cerebral decline and their relationship to driving performance and behaviours

test at a behaviour level whether reduced inhibition generates a bottleneck phenomenon that can be compensated for by increasing goal-oriented control.

PART  1  –  Thesis’  framework  

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PART  1  –  Thesis’  framework  

PART  2  –  Causation  in  traffic  security  research  

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2

C AUSATION ACROSS DISCIPLINES IN TRAFFIC SAFETY RESEARCH

Summary

In traffic safety research, identifying the cause of accidents has become important to define legal responsibilities, define modalities of reimbursements by insurances, suggest new road safety regulations, identify underlying mechanisms of risk behaviours and develop grounded prevention policies. However, in behavioural science, researchers’ concepts of causality differ considerably depending of their scientific background. I have reviewed the philosophical groundings of causality and illustrated the complexity of transposing knowledge through different fields of science. Interlevel constraints appear to be an optimal conceptual framework of causality, to help interdisciplinary researchers overcome their differences, to favour transversal research.

Keywords: Traffic medicine, behavioural science, causality, transversal research