Entwicklungspsychologie:

Beschreibung, Erklärung und Vorhersage zeitlich überdauernder, aufeinander aufbauender Veränderungen menschlichen Erlebens und Verhaltens

• Keine Altersstufe hat ‚Vorrang‘

• Kritische Lebensereignisse Veränderungen in 3 Bereichen: körperlich, emotional & sozial, kognitiv

Körperlich:

• äußeres Erscheinungsbild und Körperfunktionen

Emotional & Sozial:

• emotionale Kommunikation

• Selbst

• Freundschaften, Beziehungen und

• Moral

Kognitiv:

• intellektuelle Fähigkeiten

Entwicklung über die gesamte Lebensspanne

• Multidimensional und multidirektional

  •  Multidimensional: biologische, psychologische und soziale Faktoren

  • Multidirektional: Wachstum und Verfall

• Plastisch

  • Fähigkeit zur Veränderung immer gegeben, z. B. intellektuelle Leistungen

• Das biologische Altern (die Seneszenz)beginnt, wenn die körperlichen Strukturen ihre maximalen Fähigkeiten erreichen (Berk, 2018)

• Jegliche (positive und negative) Veränderungen der Anpassungsfähigkeit des Organismus (Baltes 1990)

• Gesundes Altern, World Health Organization (WHO):

  • ... Ist ein entwicklungsbedingter Prozess in Abwesenheit von schwerwiegenden gesundheitlichen Problemen inklusive neurologischer und psychiatrischer Erkrankungen. Es ist charakterisiert durch eine erfolgreiche Adaptation an sich ändernde Umwelten bzw. Umweltbedingungen trotz typischer neuronaler Degeneration

  • Nicht immer trennscharf / klar definiert

  • „typisches Altern“

• Längsschnittstudie

• Mehrfache Untersuchung derselben Probanden -> individuelle Entwicklung

• Drop-outs, Übungs-/Trainingseffekte, Dauer, Kohorteneffekte

• Querschnittstudie

• Personen unterschiedlichen Alters zur selben Zeit

• Effizienter, keine Drop-outs, keine Übungseffekte

• Keine individuelle Entwicklung; Gruppen-Unterschiede evtl. Kohorteneffekte

• Kohorten-Sequenz-Plan

• –  Querschnitt- und Längsschnittstudien zu verschiedenen Zeitpunkten

• –  Längsschnitt- und Querschnittvergleiche sind möglich à Kohorteneffekte erkennbar

• –  Drop-outs, Dauer, Testeffekte

• Datenschutz: besonders sensibel

• Einwilligung: ggf körperliche und geistige Einschränkungen

• Stereotypisierung: vermeiden und differenzierte Betrachtung fördern

• Diskriminierung: Alter

• Nutzen für ältere Menschen: wissenschaftlicher und praktischer Nutzen

• Inklusion und Vielfalt

  • Verantwortung der Forschenden: Arbeit im Einklang mit den geltenden Ethikstandards und Gesetzen

  • Transparenz und Zugänglichkeit: Open Science

• Cognitive changes due to changes in sensation (e.g. vision / hearing)

• Correlations of sensory abilities and cognition stronger in older vs younger adults

  • Common cause explanation: “both sets of measures are an expression of the physiological architecture of the aging brain”

    (Baltes & Lindenberger, 1997)

    • Relationship not as strong as initially suggested (Lindenberger

      & Ghisletta, 2009)àcross sectional vs longitudinal

    • Transformed over time and influenced others (e.g.

      “Dedifferentiation”)

  • Cognitive permeation hypothesis: sensory and perceptual tasks-> additional cognitive load -> reduction in available cognitive resources (Li et al., 2001; Rabbitt, 1968; Lindenberger & Ghisletta, 2009)

• Cognitive operations are limited by general processing constraints and variations in the efficiency of completion of specific processes

• Ageing: ability to process information becomes less efficient -> limited cognitive resources

• General limitations -> constrain many processes -> performance in many different tasks (encoding, retrieval, working memory, digit span etc)

• Two factors contribute to overall processing speed

  •  Limited time: discrete cognitive steps; spending more time for early operations-> limits time for later processing

  • Simultaneity: slower speed of processing -> less available information for simultaneous processing

• An efficient (fast & accurate) mental life requires the ability to limit activation to

  information most relevant to one’s goals

• Three functions of inhibition

  • Controlling to access attention’s focus

  • Deleting irrelevant information from attention and WM

  • Suppressing / restraining inappropriate responses

• the delayed distraction condition showed the lowest scores and the highest distraction costs

• 
  

Behavioral Models of Aging – Recollection Deficits

• Recognition memory – Dual-Process models (Tulving 1985; Yonelinas 2001; Yonelinas et al; 2010)

• Recollection: recognition with recollective experience

• Familiarity: recognition without recollective experience (butcher in the bus phenomenon)

• Aging: selective deficits in recollection but not familiarity

• No sufficient binding of elements of an event into an integrated episode or memory

• Memory errors when individual pieces of information are not sufficiently integrated at encoding (Dodson et al., 1998; Kelley & Sahakyan, 2003)

• ...or when source information is similar across episodes -> incorrect sources or details being retrieved with an item not originally paired together

• Aka Source-Monitoring deficits with age:

  • Contextual features e.g. color or print styles of material -> Dog, Cat, Mouse

  • Gender of a speaker

  • Location of targets etc

• Binding deficits are greater than item memory deficits (Kaszniak & Newman, 2010)

• These deficits reduce self-initiated activities that are required for efficient task completion at encoding -> difficulties in generating elaborate and distinctive memory traces

• And, without significant resources to search at retrieval, even an elaborate memory trace will not be retrieved

• No efficient processes at encoding & retrieval -> likely memory deficits

• Deficits in self-initiated processing or strategic regulation of

  learning and remembering can be task dependent (DeCarlo & Thomas 2019)

– Memory strategies improve performance through modulation of neural activity at encoding and retrieval (Berry et al., 2010; Kirchhoff et al., 2012) in MTL and PFC

• Reserve: ... a cumulative improvement, due to genetic and/or environmental factors, of neural resources that mitigates the effects of neural decline caused by ageing or age-related diseases.

• Results in the accumulation of neural resources before the brain is affected by age- related processes and to take place over a period of years

• Education: improves neural resources during childhood and young adulthood (synaptic density) and attenuates age-related cognitive decline in later adulthood

– Via health, stress, profession, lifestyle

• Reserves may also continue to build up in older ageàimportance of intellectual engagement throughout the lifespan

Brain reserve vs cognitive reserve

• Impossible to directly measure reserve -> proxies

• Education, physical activity, active leisure activities and bilingualism

• Solé-Padullés et al. (2009): Greater cognitive reserve, as measured using IQ and education–occupation as proxies, was related to larger brains and reduced activity during cognitive processing (encoding tasks)àmore effective use of cerebral networks

• Maintenance: ... the preservation of neural resources, which entails ongoing repair and replenishment of the brain in response to damage incurred at the cellular and molecular levels owing to ‘wear and tear’.

• Maintenance occurs throughout the lifespan but may become more critical in old age, as neural deterioration becomes more severe

• Timescale of maintenance processes likely depends on the neural level at which they take place (molecules, cells or systems)

• Optimal case: repair fully counteract decline VS

• Typical case: repair does not completely offset neural deterioration à gradual age- related neural deterioration.

  àEfficacy of maintenance depends on magnitude of decline and efficacy of repair

• Reserve vs maintenance – Both involve enhancing current resources – Reserve:

• Before the effects of ageing on neural function

• Shorter timescale

• Augmenting resources beyond their current level

• Cumulative influence on neural capacity and neural efficiency

  – Maintenance:

• After the effects of ageing on neural function

• Longer timescale

• Returning resources to their former level

• Influences neural mechanisms of repair and plasticity (and others not yet identified).

  àComplementary perspectives on how environmental and biological factors influence brain ageing and cognition

• individuals aged 65–80 years old who showed minimal episodic memory decline on verbal immediate free recall and delayed cued recall tasks over a 4-year period (referred to as maintainers) also showed less hippocampal volume decline over the same period than decliners

• Group of old maintainers: Mean age 68.8 years

• No significant episodic memory decline over 2 decades

• Compared with young adults (mean age 35.3 years)

• Maintainers' hippocampal activity like young adults in fMRI face–name study

• Old decliners: Longitudinal episodic memory decline

• Decliners' hippocampal activity lower during associative encoding than young adults and old maintainers

• Maintenance and decline defined independently of absolute memory and hippocampal activity

• Maintenance is a dynamic process engaging mechanisms of cellular repair and may overlap to a large degree with brain plasticity in adulthood

• In general...the cognition-enhancing recruitment of neural resources in response

  to relatively high cognitive demand

• With regard to aging... greater brain activity or connectivity without beneficial support

• 2 criteria

  • What is being compensated for?

    • Neural degeneration: gap in available resources (supply-demand gap)

    • Eyeglasses (metaphor)

  • Is enhanced activation related to a beneficial effect in cognition?

• E.g. PFC activity and WM performance

– If not provided: age-related differences, eg due to inefficient processing or dedifferentiation

• Upregulation

• Quantitative difference in neural activity between young and older adults in order to achieve an adequate level of performance

• i.e. more activity in the same brain region in older subjects

  
  

• Shift of underlying brain regions and associated behavior in older adults while performing the same apparent task; qualitative change

• E.g. recognition memory, young: high recollection / low familiarity àHC / PHC

• VS old: low recollection / high familiarity HC / PHC

• Swimming metaphor

• Both processes available in both age groups but in different use

• Reorganization

• Use of a new and alternative neural process or network in older adults (again, due to neural degeneration) to achieve an adequate level of performance

• These neural processes are not available to younger adults, which is a main difference to selection

• E.g.: LTM retrieval left PFC in young vs left and right PFC in (high performing) older

• Criteria:

• –  What is being compensated for?

• –  Enhanced activation related to behavioral effects?

• Hemispheric Asymmetry Reduction in Older Adults

• Prefrontal activity during cognitive performances tends to be less lateralized in older adults than in younger adults

• Age-related hemispheric asymmetry reductions may have a compensatory function or they may reflect a dedifferentiation process

  • Compensation: to counteract age-related neurocognitive decline

  • Dedifferentiation: difficulty in recruiting specialized neural mech.

• They may have a cognitive or neural origin, and they may reflect regional or network mechanisms

  • Psychogenic view: change in cognitive strategies

  • Neurogenic view: change in neural mechanisms

  • Network view: global reorganization of task-specific neurocognitive networks

  • Regional view: task independent local brain changes

Posterior to Anterior Shift in Aging – Grady et al., 1994:

• Grady et al., 1994: older / young adults, face matching vs location matching vs control task

• “suggesting ... more reliance by older subjects on one or more cortical networks, particularly for spatial vision, perhaps to compensate for reduced processing efficiency of occipital cortex.”

• More widespread activity reflects age-related impairment in the recruitment of specialized neural mechanisms (Li & Lindenberger, 1999)

• Links to common cause model of ”Sensory Deficit Hypothesis”

• Park et al., 2003 -> Aging reduces neural specialisation in ventral visual cortex

• More widespread activity reflects age-related impairment in the recruitment of specialized neural mechanisms (Li & Lindenberger, 1999)

• Links to common cause model of ”Sensory Deficit Hypothesis”

• WHY??

  • Loss of tuning with ageàneurons respond less selectively

  • Greater attenuation in neural response with ageàresponse to preferred stim is weakened

• Apart from visual regions also in memory-related regions (e.g. striatum/MTL) and motor system

• White Matter Microstructure Predicts Focal and Broad Functional Brain Dedifferentiation in Normal Aging (Rieck et al., 2020)

• 281 adults, ages 20–89 years, from the Dallas Lifespan Brain Study

• Cortical midline network, including medial PFC and parietal cortical regions

• Deactivated during tasks demanding external attention (e.g. semantic classification) but activated during internal reflection (e.g. thinking about oneself, mind wondering)

• Age-related difference in DMN activity more pronounced in cognitively demanding tasks (Persson et al., 2007)

  • Difficulties in modulating DMN in response to task demands

    -> Default-Executive Coupling Hypothesis of Aging – DECHA (Turner & Spreng, 2015)

    • Aging impacts the coupling of the DMN and lateral prefrontal regions that contribute to cognitive control

• Neural activity typically increases with task demands

• Not linear but quadratic relationshipàneural activity decreases when task demands exceed a tipping point (or max out neural resources)

• CRUNCH: the underlying quadratic function is shifted to the left in older humans, possible due to lower neural resources

  • Therefore, age-related differences in brain activity (increases vs decreases) and behavior strongly depend on task demands

  •  Evidence is based on several studies showing that age-related activity and connectivity patterns in the PFC depend on working memory load

• Declining neural efficiency leads older adults to engage more neural circuits

  to meet task demands

• Lower cognitive demand:

  • Older -> overactivation, including frontal or bilateral recruitment,

  • Younger -> more focal activations

• Higher cognitive demands:

  • Older -> maxed out neural resources, underactivation and performance decline

  • Younger -> shift to an overactive or bilateral pattern

• Healthy ageing is associated with “neural challenges”:e.g. white and grey matter degeneration, “functional deterioration”, including dedifferentiation or reduced cortical activity.

• This is being supported by “compensatory scaffolding”, including bilateral or enhanced recruitment, distributed processing and enhanced connectivity

• Capacity for neurogenesis, synaptogenesis, and angiogensis declines with aging, BUTall mechanisms remain functional and provide means for alternative neural circuitry

• Compensatory scaffolding is affected by experiences à new learning, enhanced cardiovascular health, sustained engagement in a mentally challenging activity, and cognitive training

  -> GOAL: to maintain a high level of cognitive function that can no longer be supported by deteriorating structures and older functional networks

• Chronological age

  • the amount of time elapsed since an individual’s birth, typically expressed in terms of months and years.

• Biological age

  • age as determined by changes in bodily structure and performance that are normative at specific ages  

    -> Influenced by

    • genetics, lifestyle, and environmental factors, diet, exercise, stress

• Rather new concept

Telomere length -> shorten with age

Epigenetic clocks -> Biochemical test to measure age, based on DNA methylation levels -> gene expression

Biomarkers -> Here: measurable indicators of physiological processes or disease (e.g. cholesterol, blood pressure, and inflammatory markers)

Functional Tests -> e.g. as grip strength, walking speed, lung capacity -> physical capabilities and health status

Imaging tests – E.g. MRI and CT scansàstructure and function of organs and tissues

=> Rather new concept and methods are no perfect measures

• Ageing results in marked changes to the structure and function of the brain

• Pronounced individual differences

• Potentially, the extent to which someone deviates from healthy brain-ageing trajectories could indicate underlying problems in outwardly healthy people and relate to the risk of cognitive ageing or age-associated brain disease

  
  

  -> reliable biomarkers of brain ageing could be of great neuroscientific and clinical value

As people get older, they are more likely to develop diseases and experience cognitive decline due to the cumulative damage to cells and tissues that occurs over time. While people who are generally healthy may reach the threshold for symptoms to appear at approximately similar ages, other people may follow different trajectories of biological ageing due to genetic or environmental factors.

- Figure 2 shows an overview of the brain age prediction process using supervised machine learning.

- The process involves training a regression model using neuroimaging data from healthy individuals labeled with their chronological age.

- The model's accuracy is validated by leaving out a proportion of the participants' images and comparing predicted values with real values using cross-validation procedures.

• Measuring brain age could provide a window on general biological ageing as a potential ageing biomarker

• In older adults: significant relationship between brain age and mortality risk, ascertained up to 7 years after scanning (Cole et al., 2017)

  • For every year that an individual’s brain was predicted to be older than their chronological age, there was a 6% increased risk of death

  • Lower grip strength, lower forced expiratory volume, and slower walking time were all significantly associated with brain age, as was a composite measure of fluid cognition

    -> brain is sensitive to general declines in health and suggests that brain age could be used as an ageing biomarker to make individualized predictions about mortality risk in older adults

• Brain development, including aging, is complex with region specific changes that do not need to be linear

• Potentially, diseases result in increases of brain ageing as a one-off ‘hit’ or a progressive acceleration of the process

• Alternatively, the presence of a disease may not cause brain ageing per se but occurs on

  top of underlying individual differences in normal brain ageing -> brain age as marker to help stratify the enrolment into clinical trials?

Coupé et al., HBM 2017; n=2,944

• Alzheimers Disease, Mild Cognitive Impairment and Obesity show the biggest differences in brain age vs. chronological age

• Significant associations with decreased brain age and markers of good health in cognitively healthy elderly (Franke et al., 2014) and the general population (Habes et al., 2016)

• The number of years of education and a self-reported measure of physical activity (number of stairs climbed daily) were significantly associated with a lower brain age in individuals aged 19–79 years (Steffener et al., 2016)

• Reductions in brain age in long-term practitioners of meditation (Luders et al., 2016) and in amateur musicians (Rogenmoser et al., 2017)

  -> interventions could slow or even reverse brain ageing, reduce the risk of future cognitive decline and age-associated disease

  BUT: often cross-sectional and no longitudinal studies

• Only time affects age -> ageing per se cannot deviate from its chronological course

• Applies to all potential ageing biomarkers limited biological variability in ageing and deviations are due to specific pathological processes, not normal ageing

Bu…

• Ageing results from cumulative biological damageàvariable exposure to the causes results in individual differences

• Ageing is the major risk for numerous diseasesàbiological ageing and diseases are linked

• If brain age can be a useful neuroscientific and clinical tool, it warrants further exploration

• Machine learning approach is overly ‘black box’àexact features of a brain scan to predict age unclear

• BUT

  • Interpreting the ‘weight maps’ derived from machine learning is complicated and does not offer a straightforward interpretation in the context of brain ageing

  • No one part of the brain is the sole driver of ageing but brain ageing is a global phenomenon.

  • Age-related brain changes are subtle, nonlinear, and spatially distributed and vary between individuals

  • Machine learning is based on a range of different brain structures which avoids focusing on the average

• Using the error in prediction (residuals) as a metric for further analysis is problematic

• More accurate models could reduce it

• BUT

  • The key to determining the validity of brain age lies in external validation with other characteristics measured in the same individuals

  • E.g. the “error metric” relates to cognitive performance, ageing fitness etc-> clinically and biologically meaningful insights

• Short term effects of pregnancy and post partum

– Rodents

• During pregnancy and postpartum: reduced neurogenesis in the dentate gyrus, and changes in volume, dendritic morphology, and cell proliferation in the hippocampus

– In humans

• During pregnancy: reduction in total brain volume, with reversion within 6 month after birth

• Postpartum: regional gray matter increases in the amygdala, hypothalamus, and prefrontal cortex

• Postpartum: regional reductions, some were positively related to maternal attachment

• Longer lasting effects

– Rodents

• Parous rats: increased hippocampal neurogenesis and less brain aging relative to nulliparous rats

– Humans

• Regional reductions in brain volume for at least 2 years post pregnancy

• Reproductive history has been linked to cortical thickness later in life

  àsome maternal brain changes revert postpartum, others are long lasting and may influence the course of neurobiological aging later in life

Methods, questions, hypothesis

• 12,021 female MRI brains from the UK Biobank: parity would be associated with apparent brain aging

• Machine learning and brain age prediction was used to test

  • –  whether a classifier could identify women as parous or nulliparous based on morphometric brain characteristics

  • –  whether brain age gap (estimated brain age minus chronological age) differed between parous (n = 9,568) and nulliparous (n = 2,453) women

  • –  Comparing women who had given 1 birth, 2 births, 3 births, 4 births, and 5 to 8 births vs nulliparous

• Mean age

  • –  full sample ± SD: 54.72 ± 7.29 y

  • –  parous 55.23 ± 7.22 y

  • –  nulliparous 52.79 ± 7.23 y

• Association between polygenic scores and 1) the probability score from the group classification and 2) brain age gap?

• The main findings of this research article are that a higher number of previous childbirths are associated with less apparent brain aging in striatal and limbic regions, including the accumbens, putamen, thalamus, hippocampus, and amygdala 9. Additionally, brain aging estimates based on three clusters were each significantly associated with the number of previous childbirths 5. These findings suggest that childbirth may have long-term effects on the brain and that these effects may vary depending on the brain region and the number of childbirths.

Regions:

• A higher number of previous childbirths were linked to less apparent brain aging in striatal and limbic regions.

• The strongest effect was found in the accumbens—a key region in the mesolimbic reward system, which plays an important role in maternal behavior.

In 235 healthy older women

• A weak but positive relationship between parity (number of children parented) and

  memory performance in mothers (rho = 0.14, p=0.039)

• Parity also associated with differences in cortical thickness in women in the para-

  hippocampus, precuneus, cuneus and peri-calcarine sulcus

  Non-parents vs parents of one child, in a sub-sample of older women (N =45) and

  men (N =35) – differences in cortical thickness

• Mothers vs non-mothers: ↓Pericalcarine Sulcus (R), ↓Middle Frontal Gyrus (L)

• Fathers vs non-fathers: ↓Anterior Cingulate (L), ↑ Temporal Pole (R)

  àpreliminary evidence that neural changes associated with parenthood persist into older age, and for women, may be related to marginally better cognitive outcomes

• Slow waves: 0.4-4.5 Hz Age-related reductions in slow wave activity (SWA)

• Over PFC and in the first NREM sleep cycles,

• 75%–80% relative to young adults

• Slope, or steepness, is shallower

• Slowing in average frequency of about 0.1 Hz

• SWA: associated with homeostatic drive to sleep after wakefulness

Sleep spindles

• Bursts of waxing and waning oscillatory activity, 12–15 Hz range

• Generated through an interaction between corticothalamic networks and the

  reticular nucleus of the thalamus Age-related changes in sleep spindlesàpronounced in final sleep cycles and PFC

• Reduced (fast and slow) density

• Peak and mean amplitude

• Age-related declines in sleep spindles even when the sleep stage from which they arise does not

  • E.g. stage 2 NREM sleep duration not impaired with age, still sleep spindle expression deteriorates

• Even with the same total amount of NREM sleep timeàsignificant age- related differences in density and amplitude of slow waves

-> age-related changes in gross macro-level sleep architecture (e.g., time and sleep stages) can, and, often are, mechanistically distinct from micro-level changes in physiological sleep oscillations

• Not all older adults suffer the same degree of sleep disruptionàlarge inter-individual variability

• Other factors may impact on vulnerability or resilience to age- associated sleep changes

• Gender

  • NREM: greater distortions in men vs women

  • E.g. Redline et al., (2004): 2,500 older adults between 37 and 92 years

    • Men: 70 vs <50 yearsà>50% reduction in SWS (no such effect in women)

    • Men vs women over 70: 3-fold deficit in SWS amount

    • Moderate reduction in REM sleep time independent of gender (>70 y)

    
    

    

• Gender also impacts age-associated changes in slow wave sleep homeostasis

• Reynolds et al. (1986):

  • 36h sleep deprivation followed by recovery sleep

  • Less homeostatic SWS in men but similar REM sleep

    -> gender-dependent and gender–independent effects emerge with age

    -> some mechanisms of sleep remain equivalent and intact (REM sleep) – but others show strong gender-dependent differences (SWS)

• Unclear: underlying mechanisms, including their onset in life, SWS slope / density of spindles / topography; paradox: women more likely to suffer subjective complaints of poor sleep

• Neurophysiological and neurochemical changes within the brainstem ascending arousal system (ARAS), thalamus, and hypothalamus, together with cortical regions, contribute to age-related sleep impairments

• Other factors

  • Sleep disorders, obesity, medication use, nocturnal

    urinary frequency, chronic pain, hormonal changes, neurodegeneration, psychiatric conditions, and medical comorbidities

  • Pathological aging: caused by degenerative neuropathology, such as beta-amyloid and neurofibrillary tangles (AD)

    
    

Brainstem and hypothalamic flip-flop model of sleep and wake regulating nuclei (Saper et al., 2010)

POA: preoptic area, LHA: lateral hypothalamic area, SCN: suprachiasmatic nucleus, LC: locus

coeruleus; VIP: vasoactive intestinal peptide

Age-related changesàstrength of sleep/wake promotion compromisedàunstable brain state 15

Thinning of gray matter within lateral frontal and superior temporal cortices -> reduced sleep time at night (Lim et al., 2016)

Gray matter volume reduction in lateral PFC -> shorter sleep time and more sleep fragmentation (Lim et al., 2016)

-> Selective and systemic age-related changes in the structure and function of subcortical and cortical brain regions contribute to the deterioration of sleep and wake regulation across the adult lifespan

• Age related structural brain atrophy -> impairments in NREM sleep oscillations

• Regional specificity -> age-related changes are not a consequence of whole brain atrophy

• Gender-specific changes in the circadian alerting signal may account for greater sleep fragmentation, less consolidated sleep, and higher daytime nap propensity in older men

  • Post mortem: fewer VIP-expressing neurons in the SCN in men across the lifespan

  
  

• The preoptic region of the hypothalamus is sexually dimorphic

  • Males: more galanin-expressing neurons within POA

  • But: in later life, this gender difference was diminished

  -> more rapid relative decline in male -> NREM differences

• Age-related declines in LC (Locus Coeruleus) is greater in male than in femaleàpotentially weakens SWS homeostasis

  • Clewett et al (2016): „Consistent with the LC-reserve hypothesis, both verbal intelligence and a composite reserve score were positively associated with LC signal intensity in older adults“

    
    

• Older adult men experience more accelerated gray matter atrophy, and more reduced metabolic activity, in the core NREM slow wave generating region of the medial PFC

• Growth hormones

  • Relate to SWS intensity and decrease in galanin- expressing neurons in POA

  • Male: reduction correlated with SWS (but not in females)

• Testosterone

  • Testosterone secretion increases with transitions into slow wave sleep in young men

  • This, and testosterone levels in general, decreases with age

  • Low testosterone levels in older men à decreased sleep efficiency and greater sleep fragmentation

  • Underlies the sex differences in atrophy associated with galanin-expressing neurons in POA

  • Rodents: galanin neuronal density is reduced by castration and restored with testosterone treatment

• Extrinsic influences

  • Alcohol intake:

    • Higher in men

    • Effects sleep quantity and quality

      
      

    • Associated with greater medial PFC atrophy

  -> Direct relationships remain elusive

• Age-related changes have significant downstream consequences for brain and body health

• Generally: sleep supports every major physiological system within the body,

  • Including immune, metabolic, thermoregulatory, endocrine, and cardiovascular function (Irwin, 2015);

  • and several cognitive and affective neural processes, such as learning and memory, emotional regulation, attention, motivation, decision making, and motor control (Walker, 2009).

• Here: focus on learning and memory

• Sleep before and after learning plays a causal role in memory processing

• NREM sleep quantity and oscillatory quality prior to learning support the restoration of next-day hippocampal-dependent encoding capacity and thus initial learning

• NREM slow wavesàsubsequent offline consolidation of hippocampal-dependent memory processing

• Age-related changes

• Others have failed to replicate the beneficial effect of brain stimulation on sleep physiology and/or memory consolidation (e.g. Eggert et al., 2013; Passmann et al., 2016; Sahlem et al., 2015)

  -> brain stimulation methods offer potential promise as intervention tools in the context of aging, but they require further refinement and demonstration of efficacy and reproducibility

Pro

• Older adults sleep significantly less when offered extremely long, enforced periods of sleep opportunity (time in bed >12 hr/day)

• After sleep-deprivation (or experimental SWS suppression), healthy older adults exhibit a smaller rebound in SWS time and SWAàlower homeostatic sleep pressure

• Older adults suffer less subjective and objective waking sleepiness following selective SWS deprivation

Con

• No lower homeostatic sleep drive in older adults, but rather, impaired sensitivity to a still present homeostatic drive

• More adenosine but fewer A1 receptors, and neuronal loss in sleep regulatory centers

• Reports of being less subjectively sleepy (older adults) are confounded by prior sleep history

• Sleep deprivation -> subjective sleepiness (first days) -> normalization (following days)

• Smaller relative impairments in cognitive performance in older adults after sleep

  deprivation may be an artifact of baseline (floor effect?)

• Deteriorated sleep quantity/quality has cognitive consequences; suggesting that older adults are not capable of generating the quality of sleep that they need to optimally maintain cognitive functions

Summary:

• No clear consensus

• Older adults do not have a reduced sleep need, but rather, an impaired ability to register and/or generate that unmet sleep need (Mander et al., 2018)

• Running increases

• dendritic complexity and the number of dendritic spines dentate gyrus, CA1 and entorhinal cortex

• Aerobic exercise increases

• resting state perfusion in the hippocampus in rodents (and young/middle-aged humans), possibly via angiogenesis, which has been linked to adults neurogenesis

Set up and Methods:

• Controls and runners were studied.

• Controls were in 30x18 cm cages.

• Runners were in 48x26 cm cages with a running wheel.

• Both groups got BrdU injections (50mgyg/day) for 10 days.

• After a month, both groups were tested in the water maze.

• Testing occurred between days 30-36 or 43-49.

• Mice were euthanized between days 54-118.

• Hemispheres were used for electrophysiology and immunocytochemistry.

Results:

• Running enhanced synaptic plasticity (LTP) in dentate gyrus and CA1 regions.

• BrdU-positive cells were higher in runners, and more of these cells were NeuN-positive (neuronal phenotype).

• No difference between groups in astrocytic fate of newborn cells.

Y -> Young, sedentary

O -> Old, sedentary

YR/OR -> Young and Running/Old and Running

• Running increased innervation (red) from caudomedial entorhinal cortex (CEnt) and lateral entorhinal cortex (LEC), proportionate to the increase in adult hippocampal neurogenesis. MS = medial septum; OB = olfactory bulb; CB = cerebellum.

Top -> Sedentary

Bottom -> Running

• Are new hippocampal neurons a functional and integral part of brain circuitry rather than just a local hippocampal phenomenon?

• New neurons receive input from perirhinal and lateral entorhinal cortex (EC), which are important for object and object-in-context information processing

• Lesion of this projection to new neurons in DG results in deficient pattern separation (Vivar et al., 2012)

Experimental Set- Up:

- Mice were taught to differentiate between stimuli on a touch screen in an operant chamber.

- Three stimulus arrays were utilized:

- Initial training with intermediate stimulus separation.

- Subsequent probe sessions with large and small stimulus separation.

- Mice were housed either with or without a running wheel.

- Study timeline was as follows:

- Initial training

- Probe sessions with different stimulus separations

- Comparison between mice with and without running wheels.

Results:

- Mice trained to achieve a 7 out of 8 correct trial criterion for big and small stimulus separation acquisition.

- Runners outperformed controls in small separation acquisition (*P<0.05), not in big separation (P>0.24).

- A potential correlation existed between trials for small separation acquisition and density of newly born neurons (P=0.13).

- Photomicrographs displayed BrdU-positive cells in control and runner dentate gyrus 10 weeks post-injection (Scale bar: 50μm).

In addition, task performance and neuro-genesis were positively correlated in adult mice

• Neurotrophins are a family of proteins that play a crucial role in the development, survival, and maintenance of nerve cells (neurons) in the nervous system. They are essential for various processes including neuronal growth, differentiation, and connectivity.

• 
  

• Voluntary wheel running upregulates expression of BDNF, IGF1 and VEGF and modulates plasticity in the hippocampus and cortex, e.g. ...

• Nerve Growths Factors -> Neurotrophins -> BDNF, IGF1, VEGF

• BDNF: brain derived neurotrophic factor

  • Major regulator of synaptic plasticity: low levels -> dysfunction (i.e. decreased synaptic plasticity) in hippocampus, cortex, extra-hippocampal limbic system and striatum

• IGF1: insulin-like growth factor 1

  • Reduced levels contribute to age-associated decline in cognitive functions

  • PE -> increases of IGF1 in skeletal muscles (transient peak after 5-10 min) -> increases IGF1 levels in the brain

• VEGF: vascular endothelial growth factor

• Positive effects

• IGF1 antibodies block the effect of physical exercise on upregulation of hippocampal BDNF expression and adult neurogenesis in rodent

• Circulating IGF1 levels are lower in older individuals with high fitness vs lower fitness

  • but the transient IGF1 increases after exercise rise to significant levels only in those individuals with higher levels of fitness

  • Nevertheless, progressive muscle training in aged humans has a significant effect on IGF1 upregulation in muscle

• Negative effects

  • Depletion of IGF1 has a protective role in mouse models of AD:

    • these animals are protected from AD-like symptoms, including reduced behavioral impairment, neuroinflammation, and neuronal loss (Cohen et al., 2009; Gontier et al., 2015)

• Lower levels of IGF1 convey longevity (Longo et al., 2015)

• The conditions under which IGF1 can mediate neuroprotective effects of physical exercise or prevent acceleration of neurodegeneration during ageing remain unclear.

- Exercise's positive impact on brain function and neurodegeneration prevention is known.

- Research by de Miguel et al. indicates runner mouse plasma contains factors, especially clusterin, mirroring exercise effects on brain and memory.

- Past studies suggested observing activity doesn't induce brain changes; recent findings suggest exercise-derived blood infusion might work.

- Exercise's effects are evident in the hippocampus, critical for learning and memory, but underlying mechanisms are unclear.

- Peripheral factors in blood are being recognized for their role in brain function, possibly influencing neurogenesis and memory.

- De Miguel et al.'s study utilized runner plasma (RP) from mice with access to running wheels, showing infused RP into sedentary mice mimicked exercise's brain benefits.

- RP-treated mice displayed improved memory, increased neural progenitor cells and astrocytes in hippocampus.

- RP treatment led to differentially expressed genes related to inflammation, plasticity, and immunity in hippocampus.

- Mass spectrometry of RP revealed proteins like clusterin (CLU), involved in various functions including inflammation, cell survival, and complement inhibition.

- CLU derived from liver and heart, binds to brain endothelial cells, consistent with exercise's blood-brain barrier support.

- CLU, like other exercise-related molecules, is complex and linked to both benefits and potential risks.

- CLU's role in reducing neuroinflammation aligns with exercise's benefits, as evidenced by increased levels in elderly humans with cognitive impairment after exercise.

- Future research should further explore exercise molecules' complexities and therapeutic potential for brain health.

• RP or control plasma (CP) collected from mice housed with or without a running wheel for four week

• Better performance in fear conditioning & Morris water maze

• HC: new granule cells and astrocyte

• Mass spectrometry of CP and RP: clusterin (CLU)àreduces neuroinflammation

-> findings were robust declines in tissue densities as a function of age in the frontal, parietal, and temporal cortices. More importantly, we found that losses in these areas were substantially reduced as a function of cardiovascular fitness, even when we statistically controlled for other moderator variables.

•  6-month fitness intervention

• Aerobic training vs toning and stretching

• Young: passive control

• ACC/SMA: anterior cingulate cortex, supplementary motor cortex; rIFG: right inferior frontal gyrus; lSTL:

  left superior temporal gyrus; AWM: anterior white matter+

• MRI based cerebral blood volume (CBV): imaging correlate of neurogenesis

• Expr. 1: mice (7 weeks), expr 2: humans (33 yrs)

Mice: 2 weeks running wheel

• Exercise has a specific impact on dentate gyrus cerebral blood volume (CBV) in mice.

• Bar graphs display relative CBV (rCBV) mean values for different hippocampal subregions in exercise and non-exercise groups over 6 weeks.

• Only the dentate gyrus displayed a significant exercise effect, with CBV peaking at week 4.

• Entorhinal cortex showed a nonsignificant CBV increase.

• High-resolution MRI visualizes hippocampal formation's external morphology and internal architecture.

• Parcellation of hippocampal subregions (entorhinal cortex, dentate gyrus, CA3, CA1) displayed.

• Hippocampal CBV map demonstrates warmer colors reflecting higher CBV.

Human: 12 weeks, four times / week, 1h

5 min warm-up, 40 min aerob, 10 cool down

• Exercise has a specific impact on dentate gyrus cerebral blood volume (CBV) in humans.

• Bar graph shows mean relative CBV (rCBV) values for hippocampal subregions before (open bars) and after exercise (filled bars).

• Similar to mice, only the dentate gyrus displayed a significant exercise effect, while the entorhinal cortex showed a nonsignificant CBV increase.

• High-resolution MRI visualizes hippocampal formation's external morphology and internal architecture.

• Parcellation of hippocampal subregions (entorhinal cortex, dentate gyrus, CA1, subiculum) displayed.

• Hippocampal CBV map demonstrates warmer colors reflecting higher CBV.

•  No effect on BDNF

• No group differences in WM

  performance

• Control: fitnesslevel &

  degeneration relationship

=> aerobic exercise training is effective at reversing hippocampal volume loss in late adulthood, which is accompanied by improved memory function and enhanced BDNF

• Maass et al., 2015, 7T MRI in older adults

  • No volume difference between moderate-to-intensive exercisers vs controls over a period of 3 month

  • Growth factor levels (BDNF, IGF1, VEGF) not affected by exercise, and changes not related to changes in fitness or perfusion

  • Changes in IGF-I levels positively correlated with HC volume changes and late verbal recallà effect independent of fitness, perfusion or their changes over time

• Ruscheweyh et al., 2011

  • Mild-to-moderate exercise: no fitness-related hippocampal changes after 6 months

• Sexton et al., 2020 – 12-weeks of aerobic exercise did not change cognitive, grey or white matter measures

• Sexton et al., 2015 – Evidence for cautious support of links between physical fitness or activity and WM structure

• Chen et al., 2020

• Exercise training may have benefits to brain health in older adults, BUT benefits are dependent upon dose of exercise intervention

-> No unequivocal evidence in favor of fitness induced plasticity and memory improvement in older humans

• Extended and chronic hormone therapy can exacerbate memory impairments and damage cells

• Aerobic fitness regimens have been shown to spare brain tissue and cognitive function

• Interactions between estrogen treatment and exercise in rodents, but unclear in humans

• Methods:

  • 54 postmenopausal women (mean age = 69.61; range = 58–80)

  • Hormone status and duration assessed via self-report

  • Aerobic fitness (VO2 peak)

  • MRI: T1 for VBM

  • Cognition

    • Wisconsin Card Sort Test (WCST): working memory, inhibition, and switching

    • Mini-Mental State Examination

• Together

• Hormone replacement therapy up to 10 years spares PFC gray matter and is associated with better executive function

• HRT beyond 10 years increases the degree of PFC deterioration and amplifies declines in executive functioning

• Higher fitness levels augment the effects of shorter durations of hormone treatment and ameliorate the declines associated with prolonged hormone treatment

• Interactive effects of fitness and hormone treatment on brain health in postmenopausal women

• Do other factors lead to interindividual variability?

  -> E.g. age, stress, presence of cerebrovascular and metabolic risk factors, genes (APOE4, BDNF), personality, nutrition, rewarding effects of physical exercise

• A combination of exercise and cognitive enrichment in mice increases protective effects against synaptotoxicity of amyloid-b protein in the hippocampus (Li et al., 2013)

• Meta-analysis, older adults with MCI: dance interventions benefit most aspects of cognitive functions, but evidence for effects on psycho-behavioral symptoms, motor function and quality of life remains unclear (Liu et al., 2021)

• The dietary flavanol epicatechin may enhance effects of running on retention of spatial memory in young rodents (van Praag, 2009) and may protect against AD (Cox et al., 2015)

• Three months intake of a high flavanol drink in older humans enhanced reaction times in pattern separation but was independent of exercise (Brickman et al., 2014)

  -> The possible positive effects of PE may be enhanced by other factors, including cognitive training, nutrition, social interaction, which all need to be addresses in future studies

• Preclinical AD: older adults, who perform “normally” on standard neuropsychological test but show AD pathology (e.g. amyloid-beta)

• Preclinical tau deposition starts in the transentorhinal cortex -> DG, CA1 and other medial temporal lobe regions

• Preclinical amyloid deposition primarily found in extramedial temporal lobe regions such as the retrosplenial region

• Mouse: 5 month of exercise reduced amyloid-beta in frontal cortex (38%), cortex at level of HC (53%), HC (40%)

• Humans: habitual physical activity associated with lower brain amyloid load, lower insulin, triglyceride, and higher brain glucose metabolism, immediate recall and visuospatial ability

• Exercise influences risk factors of AD

  • Insulin resistance: associated with reduced metabolism in MTL -> risk factor for AD

  
  

  

Improvement: plasticity in specific memory circuits (DC, EC) along with increased hippocampal perfusion and volume Preservation: exercise interventions that modify risk factors for cognitive decline and Alzheimer’s disease, such as metabolic and vascular risk factors and amyloid deposition

What is the optimal exercise regime?

Important to consider: interindividual variability and individual physical abilities -> WHO (2012) recommends vigorous exercise levels in old age of 75 min per week

“Cognitive training” -> interventions using cognitive tasks or intellectually demanding activities with the goal to enhance or maintain cognitive abilities (e.g. Bobet & Sala, 2023)

• This includes “brain-training” tasks and other activities such as music learning and video-game playing

Near transfer

• Generalization of acquired skills across two (or more) domains that are closely related to each other (e.g., studying algebra -> improve geometry)

Far transfer

• Generalization of acquired skills across domains that are only loosely related to each other (e.g., studying algebra -> improve Chinese)

- A custom-designed video game called NeuroRacer assesses multitasking performance.

- NeuroRacer-based study shows linear age-related decline in multitasking performance from 20 to 79 years.

- Older adults (60 to 85 years) improved multitasking through adaptive NeuroRacer training, surpassing even trained 20-year-olds.

- Multitasking training remediated age-related neural deficits in cognitive control as measured by EEG.

- Training extended performance benefits to untrained cognitive abilities (sustained attention, working memory).

- Increase in midline frontal theta power predicted sustained attention boost and retained multitasking improvement.

- The study highlights prefrontal cognitive control system's plasticity in aging and demonstrates a video game's role in assessing cognitive abilities, evaluating neural mechanisms, and enhancing cognition.

Expeirmental Set- Up:

• 3 Conditions: Drive Only, Sign Only and Sign and Drive

• Trainings for 1 Month, 3 Times a week (1h)

• 3 Groups: Control Group, SingleTask (drive only and sign only) and Multitasking Condition

• Testings of cognitive functions, EEG and NeuroRacer 3 times (initial, 1 Month and 6 Months after initial)

  
  

  
  

  

  
  

  Results:

  - Costs: % change in d-prime from ‘sign only’ to ‘sign and drive’àgreater cost indicates increased interference when simultaneously engaging in the two tasks

• Costs increase with age decade

• Training effects were most pronounced in the multitasking condition, followed by the task only condition

Summary and conclusion

• NeuroRacer in multitasking training mode reduced multitasking costs with gains persisting for 6 months

• Age-related deficits in neural signatures of cognitive control (EEG) were remediated by multitasking trainingàenhanced midline frontal theta power and frontal–posterior theta coherence

• Performance benefits extended to untrained cognitive control abilities (sustained attention and working memory), with an increase in midline frontal theta power predicting the training-induced boost

  -> robust plasticity of the prefrontal cognitive control system in the ageing brain

  -> first evidence of how a custom-designed video game can be used to assess cognitive abilities across the lifespan, evaluate underlying neural mechanisms, and serve as a powerful tool for cognitive enhancement

• Working memory trainings not only improved performance of the trained task (i.e., training gains), but they also improved

  • fluid intelligence

  • verbal memory

  • executive functions

  • processing speed

• Trainings based on video games have been shown to transfer to

  • executive functions

  • processing speed

  • working memory

  • attention

• Cognitive training has been associated with functional and anatomical effects

• Trainings can lead to

  • increased dopamine release in BG (Bäckman et al., 2017)

  • increased striatal BOLD activity (Dahlin et al. 2008), and

  • reduced hemodynamic activity in frontal brain regions (Heinzel et al., 2016)

Letter memory: 10 lists of randomly presented letters (A to D), task: recall the four last presented letters ->5 weeks of training (criterion)

n-back task with three levels of load (numbers: 1, 2, and 3), used as the transfer task

• The transfer effect was based on a joint training-related activity increase for the criterion (letter memory) and transfer tasks in a striatal region that also was recruited pretraining

• Transfer effects were limited to younger subjects

• Transfer can occur if the criterion and transfer tasks engage specific overlapping processing components and brain regions

• Working memory training gains in older adults most pronounced when pretest activity pattern was similar to younger controls (Heinzel et al., 2014)

  – àinterindividual variability may play an important role (see also Buitenweg et al., 2012; Jaeggi et al., 2014)

• Jaeggie et al., 2014: factors that contributed to training success -> motivation, need for cognition, preexisting ability, and implicit theories about intelligence

• Cognitive training has been associated with functional and anatomical effects

• Engvig et al. (2010): 8-weeks training in older humans to improve verbal source memory using Method of Loci

  • improved source memory

  • cortical thickness change in right fusiform and lateral orbitofrontal cortex correlated with improvement in source memory performance

• Engvig et al., (2012): 8 weeks of memory training in

  older humans

  • significant relationship between memory improvement and change in fractional anisotropy (FA)

  • indicates a role for myelin-related processes in WM plasticity

• Six-week online study with 11,430 participants (18-60 years), trained several times each week (min of 10 min a day, three times a week) on cognitive tasks to improve:

–> reasoning, memory, planning, visuospatial skills and attention

• Significant improvements in every trained cognitive tasks

• No evidence for transfer effects to untrained tasks, even when those tasks were cognitively closely related

  
  

  

First and last training scores for the six tests used to train experimental group 1 and experimental group 2.

Benchmarking scores at baseline and after six weeks of training across the three groups of participants. PAL, paired-associates learning SWM, spatial working memory VSTM, verbal short-term memory Bars represent standard deviations.

• “The best way to evaluate the empirical evidence is to carry out meta-analyses, and we discuss the conclusions of several recent meta-analyses that covered WM training, video-game playing, chess playing, music, and exergame.”

• Main objectives of Meta-Analyses

  • (a) to estimate the magnitude of an overall effect and its confidence intervals

  • (b) to quantify the consistency of the literature (i.e., whether there is variability in the findings across studies)

  • (c) to reveal the role of potential moderators

• Details on methods: see e.g. Borenstein et al., 2009; Schmidt & Hunter, 2015

• First- and second-order meta-analysis

• Heterogeneity/variance is smallà“...the results are not inconsistent and thus do not depend on differences in methodologies between researchers. ... Far-transfer effects do not exist.”

• The second-order meta-analysis (Sala et al., 2019) included

  – 14 statistically independent first-order meta-analyses (332 samples, 1,555 effect sizes, and 21,968 participants) of near- and far-transfer effects in different populations (e.g., children, adults, and older adults)

  – Results

  • Evidence for near transfer -> moderated by the age

  • Far transfer is negligible (uncorrected overall effect) or null (when placebo effects and publication bias are ruled out)

  • Within-studies and between-studies true variance are small to null with far transfer

•  Near transfer is real and moderated by the population examined

• Far transfer is due to factors that are unspecific

• “Resources should be devoted to other scientific questions—> it is not rational to invest considerable sums of money on a scientific question that has been essentially answered by the negative.”

• Green et al. (2019) recommend the opposite

• Here: physiological process in which several classes of neurotransmitters in the nervous system regulate distant diverse populations of neurons

  vs.

  direct synaptic transmission: communication between neurons

• Neuromodulatory transmitters may diffuse through large areas of the nervous system, having an effect on multiple neurons

• Neurotransmitters can be neuromodulators and vice versa

• Examples: dopamine (DA), serotonin, acetylcholine (ACh), norepinephrine (noradrenaline) etc.

• Also (but not here): is technology that acts directly upon nerves. It is the alteration—or modulation—of nerve activity by delivering electrical or pharmaceutical agents directly to a target area

• Originates: locus coeruleus

• Receptors in: thalamus, hypothalamus, striatum, neocortex, cingulate cortex, hippocampus, amygdala

• Arousal, reward system, uncertainty

  
  

  

• Originates: dorsal raphe nucleus

• Receptors in: thalamus, striatum, hypothalamus, ncl accumbens, neocortex, cingulate cortex, hippocampus, amygdala

• Mood, satiety, body temperature, sleep, nociception

• Originates: basal forebrain & brainstem

• Receptors in: brainstem, neocortex, MTL

• Attention, arousal, Learning and memory, reward, uncertainty

• Originates: substantia nigra / ventral tegmental area (SN/VTA)

• Projections: striatum, cingulate, limbic system (MTL), PFC

• Projections and functions

• SN pars compacta (SNc): caudate ncl./putamenàmotor functionsànigrostriatal system

• VTA: ncl. accumbens, septum, limbic system, medial PFCàmotivational functioning, learningà mesolimbic/mesocortical system

• SN / VTA: not well defined boundaries; VTA and SNc form a continuous layer and project to adjacent and overlapping terminal fieldsàmeso-cortico-limbic dopamine system

Projections:

LDT, laterodorsal tegmental nucleus PPTg, pedunculopontine tegmental nucleus LH, lateral hypothalamus VP, ventral pallidum SC, superior colliculus

Evidence from animal studies

• Hippocampal neurons respond to novelty in <100ms

• DA neurons in SN/VTA respond to novelty (Ljungberg, et al., 1992)

• A: Exposure to a novel environment evokes hippocampal DA release (Ihalainen et al., 1999) and it enhances the ability of a weak tetanus to induce LTP in CA1 (Li et al., 2003)

• B: LTP and LTD in amygdala, hippocampus, dorsal striatum, SN/VTA and PFC is strongly DA dependent (Lisman & Grace, 2005)

• Intra-hippocampal injection of DA-agonists increases performance in a learning

  paradigm in rats (Packard & White, 1991; Bernabeu et al., 1997) and spatial memory in aging rats (Bach et al., 1999)

The superior/middle frontal gyrus and hippocampus showed significant reduction of BOLD signal during the first few novel stimuli, whereas the signals in the fusiform and cingulate gyrus were constant.

Bunzeck et al, 2009:

• SN/VTA activations in humans were indeed driven by stimulus novelty rather than other forms of stimulus salience such as rareness, negative emotional valence, or targetness of familiar stimuli,

• whereas hippocampal responses were less selective

Bunzeck et al., 2014:

Day 1

• fMRI, encoding of scene images presented up to 5 times: novel vs familiar vsvery familiar

• Placebo vs lDOPA (100mg) vs Galantamin (8mg, acetylcholinesterase inhibitor)

  n=16/group, double blind Blood pressure, heart rate, subjective well being

  Day 2

• Recognition memory

• Results showed no group effect on recognition memory BUT DA group: correlation between reduced novelty signal (RS) and LTM

• DA involved in SN/VTA novelty processing and LTM

• Differential effects of DA and ACh

  • -  DA neurons contain cholinergic receptors

  • -  DA & ACh levels have antagonistic effects on novelty sensitive brain regions

• 100 mg levodopa improves declarative memory for pseudowords (Knecht et al. 2004)

• Degeneration of the DA system characteristic for healthy aging

  • 3% age-related decline in DA D1 and D2 receptors p.d. (starting ~40yrs)

  • SN: 2-6% ↓ DA neurons (p.d.), correlation with striatal DA

  • Episodic memory correlates with D2 receptor binding

  • Striatal volume ↓ & Hippocampal volume ↓ with age

• Recent studies in humans focus on microstructural changes

  • Age related iron accumulation in the BG

  • Age related degeneration of myelin sheaths

  • Iron is essential for the production of myelin via oligodendrocytes, and iron accumulation may cause oxidative stress and inflammation

    What are the consequences of these structural changes with regard novelty encoding / learning & memory?

- Combined behavioral paradigm (VLMT) with advanced MRI techniques used to investigate myelination and iron accumulation.

- Young (18-32 years) and healthy elderly (55-79 years) participants studied.

- Elderly group showed decreased gray matter volume, myelin reduction, and increased iron, especially in basal ganglia.

- Ventral striatum iron levels correlated negatively with myelin in elderly participants.

- Iron and myelin markers (and their ratio) predicted elderly participants' performance in VLMT.

- Suggests ventral striatum iron accumulation linked to demyelination and declarative memory impairments.

- Findings provide new insights into microstructural mechanisms behind memory decline in the elderly.

• Quantitative MRI (3T)àmultiparameter mapping (Weiskopf & Helms, 2008)

• T1, PD, MTàR2* (iron), MT (myelin)

• 31 healthy elderly (mean=67.3, SD=6.2, range: 56-78); 31 healthy young (mean=24.8, SD=2.8, range 20–31)

• VLMT (elderly only)

- No such correlation in the young (r= 0.116, p=0.535)

Whole brain regression: Relationship between microstructure and VLMT performance

- Studied age-related structural degeneration in mesolimbic system using magnetization transfer ratio (MTR).

- Correlated MTR with mesolimbic hemodynamic responses (HRs) to stimulus novelty.

- 21 healthy older adults (55-77 years) performed visual oddball paradigm during fMRI.

- HRs to novelty in right SN/VTA and right hippocampus positively correlated with SN/VTA MTR and hippocampus MTR, not amygdala MTR.

- Amygdala HR to negative emotional valence correlated with amygdala MTR, not SN/VTA or hippocampus MTR.

- Supports hippocampal-SN/VTA loop for mesolimbic novelty processing.

- Age-related degeneration of SN/VTA and hippocampus selectively affects hemodynamic activation for novelty.

- Establishes structure-function relationship in mesolimbic novelty processing.

Novelty responses in SN/VTA and HC correlate with their structural integrity (in older adults)

• Correlations between novelty-related HRs and MTRs. Novelty HR in SN/VTA (A) correlated positively with SN/VTA MTR (B) and hippocampus MTR (C) but not age (D) or amygdala MTR (E). In the hippocampus (F), novelty HR correlated positively with SN/VTA MTR (G), hippocampus MTR (H) and negatively with age (I) but not with amygdala MTR (J). Activation maps were superimposed on the group MT template (A) or the T1-weighted standard MNI brain (F) and thresholded at P 5 0.005 (uncorrected). Asterisks indicate a significant correlation at *P 5 0.05 or **P 5 0.01—n.s. abbreviates ‘‘not significant’’ (P [ 0.05)

• N=32, (67-72 years), 150 mg ldopa vs placebo

• Effect only after 6h (not 2h)

• Dose dependent, hippocampus

  dependent

Inverted u-shape effect of DA signaling and cognitive performance. Lifespan age differences in DA modulation as well as other factors, such as genotype, medication, stress and psychosis that lead to insufficient or excessive DA signaling affect the extent and pattern of DA effects on cognition.

So… can it?

• Yes, BUT it seems to depend on the integrity of the DA system and other factors in accordance with an inverted u-shape relationship of DA signaling and cognitive performance

• Other factors might be more important: motivation, curiosity, learning strategy

• Site effects of psychopharmacology

• Originates: basal forebrain & brainstem

• Receptors in: brainstem, neocortex, MTL

• Learning and memory, arousal, reward, uncertainty

• Subcortical structure located in the frontal cortex

• Can be subdivided into (Mesulam et al., 1983)

  • medial septal nucleus (Ch1)

  • vertical and horizontal limb of the diagonal band of Broca nuclei (Ch2, Ch3)

  • magnocellular complex (Ch4), which mainly includes the Nucleus basalis of Meynert (NbM) (and substantia innominata)

• NbM -> 14 mm from anterior-posterior and 16-18 mm from medial-lateral

• NbM plays an essential role in

  • the modulation of complex behaviors and cognition through the communication with limbic structures and the entire neocortex

• Cholinergic NbM neurons are

  • complexly branched and the axonal arborizations and synapses are required to remodel continuously due to their principal role in learning, memory and attention

• ACh: powerful neuromodulator involved in the regulation of neural activity in distant brain regions -> learning and memory

• Novelty processing associate with neural responses in the monkey cholinergic BF and increases in fronto-cortical and hippocampal ACh levels in rats

• High levels of ACh in the rat perirhinal cortex during the encoding of novel information promote memory performance

• High levels of ACh have a detrimental effect on

consolidation

• Removal of cholinergic inputs to the perirhinal cortex impairs object recognition in rodents (Winters and Bussey, 2005)

• Cortical ACh (acetylcholine) seems to aid in learning new declarative information rather than impacting storage or consolidation.

• Infusing scopolamine into the PRh (perirhinal cortex) before sample acquisition disrupts object recognition memory after 24 hours.

  • Scopolamine acts as a muscarinic antagonist (ACh: muscarinic and nicotinic receptors).

• Scopolamine infusion before making a choice has no noticeable impact on memory retrieval.

• Scopolamine infusions during the retention delay period enhance object recognition compared to trials with saline infusions.

• Intra-PRh scopolamine consistently improves memory, even with longer intervals between the sample and infusion (20 hours).

• Poor performance in post-sample and 3-hour pre-sample saline infusion trials implies infusion-induced interference with the acquired object trace during the sample phase.

• All observed effects of scopolamine may share a common mechanism: disruption of sample acquisition affecting subsequent recognition. Administering scopolamine just before the sample phase impairs recognition, while infusions during the retention delay or 3 hours before the sample phase might prevent interference and improve memory.

• Pharmacological stimulation of cholinergic activity (acetylcholinesterase inhibition) -> change in neural novelty signals within the medial temporal lobe (MTL)

• Cholinergic stimulationàshift in novelty responsive brain regions from medio- temporal to prefrontal areasàthe influence of the MTL and prefrontal regions in novelty processing is mediated by ACh levels

• Cholinergic antagonists can impair working memory and the encoding of novel information in explicit memory tasks, while cholinergic agonists can have opposite effects (Buccafusco et al., 2005)

• ACh also modulates attentional processing, and working memory and subsequent familiarity based recognition via changes in neural oscillations

• Importantly, cholinergic stimulation via galantamine administration slowed down retrieval speed during WM and reduced associated alpha but not theta power, suggesting a functional relationship between alpha oscillations and WM performance

• At Long- Term- Memory, this pattern was accompanied by impaired familiarity based recognition.

• These findings show that stimulating the healthy cholinergic system impairs WM and subsequent recognition, which is in line with the notion of a quadratic relationship between acetylcholine levels and cognitive functions. Moreover, our data provide empirical evidence for a specific role of alpha oscillations in acetylcholine dependent WM and associated LTM formation

Research Paradigm:

- Delay match-to-sample task aka Sternberg WM paradigm - Acetylcholinesterase inhibitor galantamine (8 mg) or a placebo before the task

- Recognition memory next day

ACh-esterase inhibition slows down RTs

ACh-esterase inhibition reduces LTM performance

• Healthy vs pathological aging, and structural vs functional aspects

• Structural degenerations of the BF can be observed during healthy aging, which leads to cognitive impairment (Düzel et al., 2010; Heys et al., 2010; Mesulam, 2004)

• E.g.

  • significant atrophy in aged rats and a loss of cholinergic BF neurons (de Lacalle et al., 1996), which, in another study, closely related to specific cognitive impairments, including spatial learning (Fischer et al., 1989)

  • BF cholinergic neurons are vulnerable to intraneuronal amyloid a—> accumulation even in cognitively unimpaired healthy older humans (Baker-Nigh et al., 2015) - preclinical AD

• Progressive neurological disorder

• Most common form of dementia, ca 60-80% of all dementia cases

• Abnormal protein deposits: amyloid plaques and tau tangles -> interfere with communication between nerve cells and cause cell death

• Early symptoms: forgetfulness, spatial confusion, and difficulty with familiar tasks; language problems, mood swings, and behavioral changes

• No cure, current treatments focus on managing symptoms and slowing the progression of the disease

• Risk factors: age, genetics, and lifestyle factors such as diet and exercise

• Early diagnosis is important for managing

• Research is ongoing

• Estimated more than 6 million people in the United States, and numbers are expected to increase (demographic change)

• the framework is about biomarkers for diagnostic purposes concerning alzheimers disease

• Biomarkers are grouped into those of b-amyloid deposition, pathologic tau, and neurodegeneration [AT(N)]

• First, amyloid deposition (accumulation of b-amyloid, A-b) =>formation of plaques

• Second, tau pathology, involving the aggregation of tau protein into neurofibrillary tangles (NFTs) -> degeneration of neurons

• Third, neurodegeneration, involving loss of brain cells and the breakdown of neural networks -> cognitive impairment and other symptoms of AD

• 2018: ...“it is premature and inappropriate to use this research framework in general medical practice. [...] should not be used to restrict alternative approaches to hypothesis testing that do not use biomarkers.“

• The study included 229 cognitively intact participants from the Alzheimer's Disease Neuroimaging Initiative dataset, categorized into four groups based on amyloid and tau PET scans.

• Participants in the Alzheimer's continuum (A-T-, A+ T-, A+ T+) were analyzed after excluding A-T+ subjects. Seed-based functional connectivity (FC) maps of BF subregions were created, focusing on Ch1-3 and Ch4 (nucleus basalis of Meynert, NBM), connected to whole-brain voxels.

• The results showed increased FC between the right Ch4 and bilateral amygdala among the three groups. FC values effectively distinguished the A-T- group from Alzheimer's continuum groups.

• Enhanced FC between Ch4 and the amygdala correlated with higher pathological burden indicated by amyloid and tau PET scans in the entire population. Additionally, better logical memory function was associated with this increased FC in the A + T+ group.

• The study suggests that functional connectivity in the NBM increases in cognitively normal elderly individuals along the Alzheimer's continuum. This increase might represent a compensatory mechanism countering AD-related changes and maintaining cognitive function.

• MCI

• Mild cognitive problems, including memory and language, that only minimally interfere with daily life

• Higher risk of developing AD

• 
  

• AD

• More pronounced cognitive impairments

  and structural degenerations

• Deposits of amyloid-à and p-tau first occur in the trans-entorhinal and entorhinal cortex (EC), located within the MTL, followed by the

  hippocampus and other cortical regions

• MRI

• Volume and shape of the hippocampus & hippocampal subfields differ between MCI vs AD vs healthy controls

• Similar but more heterogeneous effects in the EC, amygdala, and frontal and parietal cortex

• Histologic studies and in vivo

  imaging studies show early pathological changes in the NbM

• Neuronal loss in the BF

• Accumulation of amyloid-ß and pTau -> loss of neurons -> decreased levels of ACh -> interference with neuronal signaling, especially in memory encoding and attentional processing

• Major damages of cholinergic neurons of the BF in AD -> 90-95% loss in cortical ACh activity & cognitive and behavioral impairments

• In advanced AD, neural loss is more pronounced in the NbM as compared to layer-II EC

• Alzheimer's disease (AD) involves the spreading of neurodegeneration through connected brain regions, but the exact sequence is unclear.

• The conventional model suggests AD starts in the entorhinal cortex before spreading to the temporoparietal cortex.

• Previous research challenged this by showing that neurodegeneration in the nucleus basalis of Meynert (NbM), a basal forebrain area with cholinergic neurons, precedes entorhinal degeneration.

• This study compares staging models using two independent samples from the Alzheimer’s Disease Neuroimaging Initiative with CSF biomarkers and longitudinal MRI data.

• The study's predictive modeling strategy indicates that degeneration starts in NbM and spreads to the entorhinal cortex, contrary to the prevailing model.

• This finding holds across both independent samples and is influenced by CSF concentrations of pTau/amyloid-b.

• Whole-brain analysis suggests smaller baseline NbM volumes predict localized entorhinal and perirhinal cortex degeneration.

• In contrast, smaller entorhinal volumes predict degeneration in the medial temporal cortex, supporting a different staging model.

• The study suggests that degeneration of the basal forebrain cholinergic system precedes and influences broader AD-related degeneration, challenging established views of AD pathogenesis.

„normal“ vs. „abnormal“ based on ratio of pTau/amyloid-beta (CSF)

• NbM baseline volume predicted longitudinal EC degenerations,

  implying a trans-synaptic spread

  of amyloid-à & pTau starting in the

  NbM

• Compatible with work in animals and adds a crucial upstream link to the subsequent spread from EC to

  other MTL structures

Slopes of the NbMàEC and ECàNbM robust regression models were different in (A) the pooled abnormal CSF group (aCSF), but

(B) not in the pooled normal CSF group

• Less gray matter in the NbM of AD patients but only weak evidence in MCI

• MCI and AD both showed reduced gray matter in the MTL, including the hippocampus and amygdala, with hints towards more pronounced amygdala effects in AD

  -> structural degeneration of the cholinergic BF and interconnected MTL play a critical role in the progression of AD

• Personality refers to the enduring characteristics and behavior that comprise a person’s unique adjustment to life, including major traits, interests, drives, values, self-concept, abilities, and emotional patterns.

• Various theories explain the structure and development of personality in different ways, but all agree that personality helps determine behavior.

• The field of personality psychology studies the nature and definition of personality as well as its development, structure and trait constructs, dynamic processes, variations (with emphasis on enduring and stable individual differences), and maladaptive forms.

• Openness to experience

  • open to new ideas, experiences, ways of thinking

  • curious, imaginative, and creative vs conventional

• Conscientiousness

  • responsible, organized, goal-oriented

  • reliable, hardworking, disciplined vs disorganized, impulsive

• Extraversion

  • outgoing, sociable, energetic

  • talkative, assertive, enjoy being around other people vs reserved and introverted

• Agreeableness

  • cooperative, empathetic, considerate of others

  • warm, compassionate, friendly vs competitive and confrontational

• Neuroticism

  • negative emotions such as anxiety, fear, and sadness

  • prone to stress, worry vs emotionally stable

• NEO-PI-R http://www.psychomeda.de/online-tests/persoenlichkeitstest.html

• Big Five Inventory (BFI-10) https://tinyurl.com/mr28uzaj

• Personality, especially the dimensions of neuroticism and conscientiousness, has prospectively predicted the risk of incident Alzheimer’s disease (AD).

• Such a relationship could be explained by

  • personality and AD risk having a common cause such as a gene;

  • personality creating a predisposition for AD through health behavior or inflammation

  • personality exerting a pathoplastic effect on the cognitive consequences of neuropathology

  • AD and personality change existing on a disease spectrum that begins up to decades before diagnosis

• Evidence is sparse but suggests predisposition and/or pathoplastic relationships

• The pathogenic process of AD is still a matter of some debate

• Focus on plaques as causal has been called into question

• Phase III pharmacological clinical trials: clinical progression of mild-to-moderate AD was not affected even when the drug reduced Aà and tau relative to controls

• The primary risk factors for AD are age, genetic polymorphisms, and family history

• ...clarify the nature of the personality–AD relationship and exploit advances in neuroimaging and biomarkers

WHY does personality correlate with AD?

• Common cause

• Personality and AD have a common cause (shared etiology) but are not causally

  related to each other

• Genetic polymorphisms provide a “common core liability”

• APOE is the most common genetic predictors of AD risk

  • Carriers of 2 vs no APOE !4 alleles: 8–12 times the risk for AD

  • BUT: there was no relationship with any dimension in the FFM , and

    statistical control did not affect the relationship between personality and AD risk

• Polymorphisms affecting proneness to inflammation have been implicated in AD and may also be associated with personality

• Genes account for ca 1/3 to personality – environment/experience the rest

  • if life experiences modify personality and AD risk independently, a common cause model would be supported

• Predisposition

• Personality is a risk or protective factor for the development of AD

• HOW? Mediation via health behaviour:

• AD attributable to risk factors related to health behaviour

  • diabetes, hypertension, obesity, smoking, depression, cognitive inactivity, and physical inactivity (Barnes & Yaffe, 2011)

  • Personality, especially conscientiousness, is correlated with all of them

• Conscientiousness, associated with

  • healthier behaviour: alcohol and drug use, diet, physical activity, and tobacco use (Bogg & Roberts, 2004)

  • higher educational attainment

• Neuroticism

  • higher risk for depression (Klein et al., 1993), smoking (Lahey, 2009)

• Openness to experience

  • Correlates with cognitive activityàhigher education in kids

• Pathoplastic

• Personality influences the presentation, course, or outcome of AD

• AD neuropathology not perfectly related to neuropsychology

• Suggests: resilience to neuropathology is associated with personality

Evidence?

•  Older adults with AD neuropathology but cognitively intact were less neurotic and more conscientious than control group (AD and cogn. impaired)

• Older adults lower in neuroticism appeared to be protected from the effects of A -> deposition on memory complaints, but no such relationship for conscientiousness

• Interaction between APOE and personality may also represent a form of pathoplastic relationship

  • APOE !4 carriers high in neuroticism had a hazard ratio for AD of 8.68 vs low in neuroticism had a hazard ratio of 1.80 (no effect of neuroticism in people without the APOE !4 gene)

  • - Cognitive reserve can have protective effects

    • Conscientiousness and openness/intellect likely contribute to higher cognitive reserve through their effects on educational attainment (Hampson et al., 2007)

    • Other personality dimensions may also build reserve through physical and social activity

• Spectrum

• Personality is a prodrome or other subclinical manifestation of AD

Evidence?

– Personality change is common in AD

• Marked decreases in conscientiousness

• Moderate increases in neuroticism

• Decreases in extraversion, agreeableness, and openness

• And detectable even in the earliest stages by self-report or informant report

  – But

• AD neuropathology may begin decades before the onset of disease

• Personality during preclinical phase and longer follow-up periods suggest that baseline personality reflected more than prodromal personality change

Background

• High neuroticism associated with a greater risk of dementia, and an active/socially integrated lifestyle with a lower risk of dementia

Aim

• Explore the separate and combined effects of neuroticism and extraversion on the risk of dementia, and to examine whether lifestyle factors may modify this association

Methods

• Population-based cohort (506 older people with no dementia, Kungsholmen Project, Stockholm, Sweden), followed up for an average of 6 years

• Personality traits assessed with Eysenck Personality Inventory

• Dementia was diagnosed by specialists according to DSM-III-R criteria

• Lifestyle factors: leisure activities and social network

Conclusion

• Low neuroticism in combination with high extraversion is the personality trait associated with the lowest dementia risk

• Among socially isolated individuals even low neuroticism alone seems to decrease dementia risk

• Possible mechanisms:

  • High neuroticisms -> stress (HPA axis) -> hippocampal neurodegeneration

  • Extraversion: coping strategies, social support

• What about other personality dimensions? What about other lifestyles?

• Powerful form of intrinsic motivation enabling us to seek and explore novel information, thereby shaping individual development

• Many studies in children and adolescents, but the lifelong developmental trajectories, underlying neural mechanisms, and possible age-related changes remain unclear

• Conceptually

  • Epistemic curiosity: new knowledge, “information

    gap”

  • Perceptual curiosity: perceptual experiences

  • Social curiosity: information about other people

  • Trait curiosity: stable individual’s personality

  • State curiosity: transient context specific state

• Intrinsic motivation: state epistemic curiosity drives long-term memory via the DA mesolimbic system

• State EC has positive effects on LTM in both young and older humans

  • fits to stable EC traits across the life-span and a close relationship between state and trait curiosity

  • BUT – the DA mesolimbic system typically degenerates during healthy aging, which should reduce the effects of state EC on LTM in older adults

• Study 1

• Replicate the behavioral effect of state episodic curiosity on LTM in HY and HO adults

• Trait curiosity is supposed to be a rather stable personality trait

• Interest (I) and deprivation (D) type epistemic curiosity (Litman & Mussel, 2013)

• I-EC: intrinsic pleasure of learning

• E.g. “I enjoy exploring new ideas”

• D-EC: uncomfortable intense feeling of “need to know”

• E.g. “Difficult problems can keep me up all night thinking about solutions"

• Since D-EC reflects an unsatisfied need-like state, it is hypothesized to be a stronger motive

• 10 item questionnaire

• I- and D-type EC play a role in academic goal-setting and learning , job-related motivation and performance, and overall intellectual development and growth

• Study 2

  • But the relationship between state EC, trait EC and formal education is unclear

• Study 1

• State curiosity promotes long-term memory in young and older humans

• Possible via the dopaminergic mesolimbic system

• Despite typical age-related structural degeneration – possibly via neural compensation

• Upregulation, Selection, Reorganization (Cabeza et al., 2018)

• Other possibilities: larger existing knowledge network (old); differences in emotional value / cost / perceived competence (Murayama, 2022)

• Online study (SoSci Survey, https://www.soscisurvey.de)

• n=231 human subjects (34 excluded à 196)

• 18-82 years (w=131, m=59)

• Trait-curiosity questionnaire (Litman & Mussel, 2013): I-EC & D-EC

• State-curiosity questionnaire: 21 trivia questions had to be rated on

  • Knowing the answer

  • Curiosity to know the answer on a scale of 1-6

• Sociodemographic information

  • Age, gender etc

  • Highest formal educational degree

• Abgang ohne Abschluss, Hauptschulabschluss, Realschulabschluss, Lehre, Fachabitur, Abitur, Fachhochschul- und Hochschulabschluss

• Study 2

• Further evidence for a conceptual distinction between state vs trait curiosity

  and between interest vs deprivation type trait curiosity

• State curiosity appears to be the driving force behind formal education, which is in line with a strong link between state curiosity and cognitive abilities

• A: American adults of all ages typically eat a broadly unhealthy diet relative to national recommendations

• B: adults of all ages typically consume less than the recommended portions of most healthy food groups

  • A: Whole grains, B: Dairy, C: Seafood, D: Vegetables, E: Fruit, F: Oils, G: Nuts, seeds, soy, H: Protein, I: Meat, poultry, eggs, J: Refined grains, K: SoFAS.

EAR: estimated average recommendation AI: adequate intake

• To meet the same or increased absolute intakes of protein and micronutrients in a diet containing a diminishing level of energy, the proportion of nutrient-dense foods has to be increased,

• And, in parallel: decrease of greater magnitude in the quantity of low- nutrient foods.

  -> healthier diet is needed in older age to counterbalance decreasing energy requirements

• There is a negative cycle between functional losses and inadequate nutrition in older adults that accelerates unhealthy aging.

• Sarcopenia

  • age-associated loss in skeletal muscle mass

    and function

  • cause of decreases in movement, physiological capacity, and functional performance, increased disability and mortality

  • Cause: multifactorial including inadequate nutrition, low physical activity, inflammation, and multiple NCDs and other comorbidities

  • Reduced energy requirements and limited ability to shop for food and prepare meals

• Poor vision – limits the capability to purchase, prepare, and consume healthy food

• Reduced dental health, taste, smell, and hunger – reduce the drive to eat

• Medications

  • that impact food intake and have digestive problems, including gastric atrophy, chronic constipation, and/or malabsorption (64,65)that negatively impact appetite and nutrient absorption

• Changes in homeostatic mechanisms regulating thirst sensation and renal water absorption

  • resulting in a higher risk of dehydration

• Poverty and food insecurity

  • make it harder to purchase the nutrient-rich foods that are both more necessary and more expensive

• Social isolation – Limits food preparation and predicts unhealthy dietary intake

• Unhealthy nutrition throughout life, but especially in old age

• Low energy requirements contribute to unhealthy nutrition in older adults

• Functional losses are contributors to unhealthy nutrition in older adults

• Socioeconomic factors are contributors to unhealthy nutrition in older adults

  àCan dietary patterns, nutrients, and weight management be used for prevention and treatment of aging-associated diseases and conditions

Preventing Cognitive Decline and/or Alzheimers/Dementia via

• a healthy BMI

• dietary patterns -> mediterranian diet

• and for Alzheimers -> low saturated fat

Dietary patterns and nutrients

prevention

• High dietary flavanol (sekundäre Pflanzenstoffe) intakes over 2 decades -> associated with a reduced risk of Alzheimer disease and related dementias

• Greater adherence (Einhaltung) to a Mediterranean diet for > 5y -> associated with

  • a 1–3-fold reduction in risk of frailty (Gebrechlichkeit)

  • a 30% reduction in risk of a major cardiovascular event, and

  • a 41% reduced risk of incident advanced age-related macular degeneration

disease progression

• Randomized trials indicate positive effects on:

  • sarcopenia, osteoporosis and fractures, age-related macular degeneration, type 2 diabetes, and chronic constipation

• BUT, not all age-related diseases and conditions that are apparently prevented by healthy nutrition can also be treated after their diagnosis

Dietary patterns and nutrients – what about supplementation with specific nutrients?

• Mean intake of vitamin D in US women aged 51–70 is only about one-fourth of the RDA, even worse for ages 71 y

• Mean calcium intake is less than one-third of the RDA in older adults

• Vitamin B-12 deficiency due to chronic atrophic gastritis due to gastric acid–blocking drugs that inhibit digestion of foodbased vitamin B-12

  
  

  -> Specific, individual, supplementation with specific nutrients can be useful

Weight management

• BMI values above 25.0 are strongly associated with increased risk of a wide range of age associated diseases

• Older adults with obesity (41% of adults >60 y) are at higher risk of

  • frailty and osteoarthritis -> more functional limitations

  • all the major NCDs (Non-Communicable Diseases),

    • cognitive decline and dementia, obstructive sleep apnea

    • sensory impairments (age-related macular degeneration, cataracts, diabetic retinopathy, and hearing loss), and

    • urinary incontinence

• Progressive increase in the risks of type 2 diabetes, cardiovascular disease, and cancer with every year that obesity is maintained

• Conversely (umgekehrt): reduced energy intake promotes healthy aging

  • E.g.: low energy intake promotes favorable changes in age- related biomarkers (tumor necrosis factor-!, cardiometabolic risk factors

2 year trial of calory restriction in non-obese humans

Changes in total cholesterol (A), mean triglycerides (B), HOMA-IR (C), and mean blood pressure (D) at month 12 and month 24

Effects on cognitive function of consuming components from each food group across the life course, according to the strength of the reviewed evidence:

• No evidence for particular food groups in specific age groups

• Most evidence is based on cross-sectional analyses

• Scarce evidence of effects of chronic intake of specific foods or multiple foods within a food group

• Self-report and recall-based food frequency questionnaires