Global workspace theory

From Wikipedia, the free encyclopedia

Global workspace theory (GWT) is a simple cognitive architecture that has been developed to account qualitatively for a large set of matched pairs of conscious and unconscious processes. It was proposed by Bernard Baars (1988, 1997, 2002). Brain interpretations and computational simulations of GWT are the focus of current research.

GWT resembles the concept of working memory, and is proposed to correspond to a "momentarily active, subjectively experienced" event in working memory (WM)—the "inner domain in which we can rehearse telephone numbers to ourselves or in which we carry on the narrative of our lives. It is usually thought to include inner speech and visual imagery." (in Baars, 1997).

The theater metaphor

GWT can be explained in terms of a "theater metaphor". In the "theater of consciousness" a "spotlight of selective attention" shines a bright spot on stage. The bright spot reveals the contents of consciousness, actors moving in and out, making speeches or interacting with each other. The audience is not lit up—it is in the dark (i.e., unconscious) watching the play. Behind the scenes, also in the dark, are the director (executive processes), stage hands, script writers, scene designers and the like. They shape the visible activities in the bright spot, but are themselves invisible. Baars argues that this is distinct from the concept of the Cartesian theater, since it is not based on the implicit dualistic assumption of "someone" viewing the theater, and is not located in a single place in the mind (in Blackmore, 2005).

The model

GWT involves a fleeting memory with a duration of a few seconds (much shorter than the 10–30 seconds of classical working memory). GWT contents are proposed^{[citation needed]} to correspond to what we are conscious of, and are broadcast to a multitude of unconscious cognitive brain processes, which may be called receiving processes. Other unconscious processes, operating in parallel with limited communication between them, can form coalitions which can act as input processes to the global workspace. Since globally broadcast messages can evoke actions in receiving processes throughout the brain,^{[citation needed]} the global workspace may be used to exercise executive control to perform voluntary actions. Individual as well as allied processes compete for access to the global workspace,^{[citation needed]} striving to disseminate their messages to all other processes in an effort to recruit more cohorts and thereby increase the likelihood of achieving their goals.

Baars (1997) suggests that the global workspace "is closely related to conscious experience, though not identical to it." Conscious events may involve more necessary conditions, such as interacting with a "self" system, and an executive interpreter in the brain, such as has been suggested by a number of authors including Michael S. Gazzaniga.

Nevertheless, GWT can successfully model a number of characteristics of consciousness, such as its role in handling novel situations, its limited capacity, its sequential nature, and its ability to trigger a vast range of unconscious brain processes. Moreover, GWT lends itself well to computational modeling. Stan Franklin's IDA model is one such computational implementation of GWT. See also Dehaene et al. (2003) and Shanahan (2006).

GWT also specifies "behind the scenes" contextual systems, which shape conscious contents without ever becoming conscious, such as the dorsal cortical stream of the visual system. This architectural approach leads to specific neural hypotheses. Sensory events in different modalities may compete with each other for consciousness if their contents are incompatible. For example, the audio and video track of a movie will compete rather than fuse if the two tracks are out of sync by more than 100 ms., approximately. The 100 ms time domain corresponds closely with the known brain physiology of consciousness, including brain rhythms in the alpha-theta-gamma domain, and event-related potentials in the 200-300 ms domain.^[1]

Global Neuronal Workspace

Stanislas Dehaene extended the global workspace with the "neuronal avalanche" showing how sensory information gets selected to be broadcast throughout the cortex. ^[2] Many brain regions, the prefrontal cortex , anterior temporal lobe, inferior parietal lobe, and the precuneus all send and receive numerous projections to and from a broad variety of distant brain regions, allowing the neurons there to integrate information over space and time. Multiple sensory modules can therefore converge onto a single coherent interpretation, for example, a "red sports car zooming by". This global interpretation is broadcast back to the global workspace creating the conditions for the emergence of a single state of consciousness, at once differentiated and integrated.

Criticism

Susan Blackmore challenged the concept of stream of consciousness in several papers, by stating that "When I say that consciousness is an illusion I do not mean that consciousness does not exist. I mean that consciousness is not what it appears to be. If it seems to be a continuous stream of rich and detailed experiences, happening one after the other to a conscious person, this is the illusion."^[3] Blackmore also quotes William James: "The attempt at introspective analysis in these cases is in fact like seizing a spinning top to catch its motion, or trying to turn up the gas quickly enough to see how the darkness looks."^{[citation needed]}

Baars is in agreement with these points. The continuity of the "stream of consciousness" may in fact be illusory, just as the continuity of a movie is illusory. Nevertheless, the seriality of mutually incompatible conscious events is well supported by objective research over some two centuries of experimental work. A simple illustration would be to try to be conscious of two interpretations of an ambiguous figure or word at the same time. When timing is precisely controlled, as in the case of the audio and video tracks of the same movie, seriality appears to be compulsory for potentially conscious events presented within the same 100 ms interval.^{[citation needed]}

J. W. Dalton has criticized the global workspace theory on the grounds that it provides, at best, an account of the cognitive function of consciousness, and fails even to address the deeper problem of its nature, of what consciousness is, and of how any mental process whatsoever can be conscious: the so-called "hard problem of consciousness".^[4] A. C. Elitzur has argued, however, "While this hypothesis does not address the 'hard problem', namely, the very nature of consciousness, it constrains any theory that attempts to do so and provides important insights into the relation between consciousness and cognition."^[5]

New work by Richard Robinson shows promise in establishing the brain functions involved in this model and may help shed light on how we understand signs or symbols and reference these to our semiotic registers.^[6]

What Is Consciousness?

Scientists are beginning to unravel a mystery that has long vexed philosophers

• By Christof Koch | Scientific American June 2018 Issue
• Original post:  https://www.scientificamerican.com/article/what-is-consciousness/

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Credit: Chris Gash

Consciousness is everything you experience. It is the tune stuck in your head, the sweetness of chocolate mousse, the throbbing pain of a toothache, the fierce love for your child and the bitter knowledge that eventually all feelings will end.

The origin and nature of these experiences, sometimes referred to as qualia, have been a mystery from the earliest days of antiquity right up to the present. Many modern analytic philosophers of mind, most prominently perhaps Daniel Dennett of Tufts University, find the existence of consciousness such an intolerable affront to what they believe should be a meaningless universe of matter and the void that they declare it to be an illusion. That is, they either deny that qualia exist or argue that they can never be meaningfully studied by science.

If that assertion was true, this essay would be very short. All I would need to explain is why you, I and most everybody else is so convinced that we have feelings at all. If I have a tooth abscess, however, a sophisticated argument to persuade me that my pain is delusional will not lessen its torment one iota. As I have very little sympathy for this desperate solution to the mind-body problem, I shall move on.

The majority of scholars accept consciousness as a given and seek to understand its relationship to the objective world described by science. More than a quarter of a century ago Francis Crick and I decided to set aside philosophical discussions on consciousness (which have engaged scholars since at least the time of Aristotle) and instead search for its physical footprints. What is it about a highly excitable piece of brain matter that gives rise to consciousness? Once we can understand that, we hope to get closer to solving the more fundamental problem.

We seek, in particular, the neuronal correlates of consciousness (NCC), defined as the minimal neuronal mechanisms jointly sufficient for any specific conscious experience. What must happen in your brain for you to experience a toothache, for example? Must some nerve cells vibrate at some magical frequency? Do some special “consciousness neurons” have to be activated? In which brain regions would these cells be located?

Neuronal Correlates of Consciousness

When defining the NCC, the qualifier “minimal” is important. The brain as a whole can be considered an NCC, after all: it generates experience, day in and day out. But the seat of consciousness can be further ring-fenced. Take the spinal cord, a foot-and-a-half-long flexible tube of nervous tissue inside the backbone with about a billion nerve cells. If the spinal cord is completely severed by trauma to the neck region, victims are paralyzed in legs, arms and torso, unable to control their bowel and bladder, and without bodily sensations. Yet these tetraplegics continue to experience life in all its variety—they see, hear, smell, feel emotions and remember as much as before the incident that radically changed their life.

Or consider the cerebellum, the “little brain” underneath the back of the brain. One of the most ancient brain circuits in evolutionary terms, it is involved in motor control, posture and gait and in the fluid execution of complex sequences of motor movements. Playing the piano, typing, ice dancing or climbing a rock wall—all these activities involve the cerebellum. It has the brain's most glorious neurons, called Purkinje cells, which possess tendrils that spread like a sea fan coral and harbor complex electrical dynamics. It also has by far the most neurons, about 69 billion (most of which are the star-shaped cerebellar granule cells), four times more than in the rest of the brain combined.

What happens to consciousness if parts of the cerebellum are lost to a stroke or to the surgeon's knife? Very little! Cerebellar patients complain of several deficits, such as the loss of fluidity of piano playing or keyboard typing but never of losing any aspect of their consciousness. They hear, see and feel fine, retain a sense of self, recall past events and continue to project themselves into the future. Even being born without a cerebellum does not appreciably affect the conscious experience of the individual.

All of the vast cerebellar apparatus is irrelevant to subjective experience. Why? Important hints can be found within its circuitry, which is exceedingly uniform and parallel (just as batteries may be connected in parallel). The cerebellum is almost exclusively a feed-forward circuit: one set of neurons feeds the next, which in turn influences a third set. There are no complex feedback loops that reverberate with electrical activity passing back and forth. (Given the time needed for a conscious perception to develop, most theoreticians infer that it must involve feedback loops within the brain's cavernous circuitry.) Moreover, the cerebellum is functionally divided into hundreds or more independent computational modules. Each one operates in parallel, with distinct, nonoverlapping inputs and output, controlling movements of different motor or cognitive systems. They scarcely interact—another feature held indispensable for consciousness.

One important lesson from the spinal cord and the cerebellum is that the genie of consciousness does not just appear when any neural tissue is excited. More is needed. This additional factor is found in the gray matter making up the celebrated cerebral cortex, the outer surface of the brain. It is a laminated sheet of intricately interconnected nervous tissue, the size and width of a 14-inch pizza. Two of these sheets, highly folded, along with their hundreds of millions of wires—the white matter—are crammed into the skull. All available evidence implicates neocortical tissue in generating feelings.

We can narrow down the seat of consciousness even further. Take, for example, experiments in which different stimuli are presented to the right and the left eyes. Suppose a picture of Donald Trump is visible only to your left eye and one of Hillary Clinton only to your right eye. We might imagine that you would see some weird superposition of Trump and Clinton. In reality, you will see Trump for a few seconds, after which he will disappear and Clinton will appear, after which she will go away and Trump will reappear. The two images will alternate in a never-ending dance because of what neuroscientists call binocular rivalry. Because your brain is getting an ambiguous input, it cannot decide: Is it Trump, or is it Clinton?

If, at the same time, you are lying inside a magnetic scanner that registers brain activity, experimenters will find that a broad set of cortical regions, collectively known as the posterior hot zone, is active. These are the parietal, occipital and temporal regions in the posterior part of cortex [see graphic below] that play the most significant role in tracking what we see. Curiously, the primary visual cortex that receives and passes on the information streaming up from the eyes does not signal what the subject sees. A similar hierarchy of labor appears to be true of sound and touch: primary auditory and primary somatosensory cortices do not directly contribute to the content of auditory or somatosensory experience. Instead it is the next stages of processing—in the posterior hot zone—that give rise to conscious perception, including the image of Trump or Clinton.

More illuminating are two clinical sources of causal evidence: electrical stimulation of cortical tissue and the study of patients following the loss of specific regions caused by injury or disease. Before removing a brain tumor or the locus of a patient's epileptic seizures, for example, neurosurgeons map the functions of nearby cortical tissue by directly stimulating it with electrodes. Stimulating the posterior hot zone can trigger a diversity of distinct sensations and feelings. These could be flashes of light, geometric shapes, distortions of faces, auditory or visual hallucinations, a feeling of familiarity or unreality, the urge to move a specific limb, and so on. Stimulating the front of the cortex is a different matter: by and large, it elicits no direct experience.

A second source of insights are neurological patients from the first half of the 20th century. Surgeons sometimes had to excise a large belt of prefrontal cortex to remove tumors or to ameliorate epileptic seizures. What is remarkable is how unremarkable these patients appeared. The loss of a portion of the frontal lobe did have certain deleterious effects: the patients developed a lack of inhibition of inappropriate emotions or actions, motor deficits, or uncontrollable repetition of specific action or words. Following the operation, however, their personality and IQ improved, and they went on to live for many more years, with no evidence that the drastic removal of frontal tissue significantly affected their conscious experience. Conversely, removal of even small regions of the posterior cortex, where the hot zone resides, can lead to a loss of entire classes of conscious content: patients are unable to recognize faces or to see motion, color or space.

So it appears that the sights, sounds and other sensations of life as we experience it are generated by regions within the posterior cortex. As far as we can tell, almost all conscious experiences have their origin there. What is the crucial difference between these posterior regions and much of the prefrontal cortex, which does not directly contribute to subjective content? The truth is that we do not know. Even so—and excitingly—a recent finding indicates that neuroscientists may be getting closer.

The Consciousness Meter

An unmet clinical need exists for a device that reliably detects the presence or absence of consciousness in impaired or incapacitated individuals. During surgery, for example, patients are anesthetized to keep them immobile and their blood pressure stable and to eliminate pain and traumatic memories. Unfortunately, this goal is not always met: every year hundreds of patients have some awareness under anesthesia.

Another category of patients, who have severe brain injury because of accidents, infections or extreme intoxication, may live for years without being able to speak or respond to verbal requests. Establishing that they experience life is a grave challenge to the clinical arts. Think of an astronaut adrift in space, listening to mission control's attempts to contact him. His damaged radio does not relay his voice, and he appears lost to the world. This is the forlorn situation of patients whose damaged brain will not let them communicate to the world—an extreme form of solitary confinement.

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Credit: Mesa Schumacher

In the early 2000s Giulio Tononi of the University of Wisconsin–Madison and Marcello Massimini, now at the University of Milan in Italy, pioneered a technique, called zap and zip, to probe whether someone is conscious or not. The scientists held a sheathed coil of wire against the scalp and “zapped” it—sent an intense pulse of magnetic energy into the skull—inducing a brief electric current in the neurons underneath. The perturbation, in turn, excited and inhibited the neurons' partner cells in connected regions, in a chain reverberating across the cortex, until the activity died out. A network of electroencephalogram (EEG) sensors, positioned outside the skull, recorded these electrical signals. As they unfolded over time, these traces, each corresponding to a specific location in the brain below the skull, yielded a movie.

These unfolding records neither sketched a stereotypical pattern, nor were they completely random. Remarkably, the more predictable these waxing and waning rhythms were, the more likely the brain was unconscious. The researchers quantified this intuition by compressing the data in the movie with an algorithm commonly used to “zip” computer files. The zipping yielded an estimate of the complexity of the brain's response. Volunteers who were awake turned out have a “perturbational complexity index” of between 0.31 and 0.70, dropping to below 0.31 when deeply asleep or anesthetized. Massimini and Tononi tested this zap-and-zip measure on 48 patients who were brain-injured but responsive and awake, finding that in every case, the method confirmed the behavioral evidence for consciousness.

The team then applied zap and zip to 81 patients who were minimally conscious or in a vegetative state. For the former group, which showed some signs of nonreflexive behavior, the method correctly found 36 out of 38 patients to be conscious. It misdiagnosed two patients as unconscious. Of the 43 vegetative-state patients in which all bedside attempts to establish communication failed, 34 were labeled as unconscious, but nine were not. Their brains responded similarly to those of conscious controls—implying that they were conscious yet unable to communicate with their loved ones.

Ongoing studies seek to standardize and improve zap and zip for neurological patients and to extend it to psychiatric and pediatric patients. Sooner or later scientists will discover the specific set of neural mechanisms that give rise to any one experience. Although these findings will have important clinical implications and may give succor to families and friends, they will not answer some fundamental questions: Why these neurons and not those? Why this particular frequency and not that? Indeed, the abiding mystery is how and why any highly organized piece of active matter gives rise to conscious sensation. After all, the brain is like any other organ, subject to the same physical laws as the heart or the liver. What makes it different? What is it about the biophysics of a chunk of highly excitable brain matter that turns gray goo into the glorious surround sound and Technicolor that is the fabric of everyday experience?

Ultimately what we need is a satisfying scientific theory of consciousness that predicts under which conditions any particular physical system—whether it is a complex circuit of neurons or silicon transistors—has experiences. Furthermore, why does the quality of these experiences differ? Why does a clear blue sky feel so different from the screech of a badly tuned violin? Do these differences in sensation have a function, and if so, what is it? Such a theory will allow us to infer which systems will experience anything. Absent a theory with testable predictions, any speculation about machine consciousness is based solely on our intuition, which the history of science has shown is not a reliable guide.

Fierce debates have arisen around the two most popular theories of consciousness. One is the global neuronal workspace (GNW) by psychologist Bernard J. Baars and neuroscientists Stanislas Dehaene and Jean-Pierre Changeux. The theory begins with the observation that when you are conscious of something, many different parts of your brain have access to that information. If, on the other hand, you act unconsciously, that information is localized to the specific sensory motor system involved. For example, when you type fast, you do so automatically. Asked how you do it, you would not know: you have little conscious access to that information, which also happens to be localized to the brain circuits linking your eyes to rapid finger movements.

Toward a Fundamental Theory

GNW argues that consciousness arises from a particular type of information processing—familiar from the early days of artificial intelligence, when specialized programs would access a small, shared repository of information. Whatever data were written onto this “blackboard” became available to a host of subsidiary processes: working memory, language, the planning module, and so on. According to GNW, consciousness emerges when incoming sensory information, inscribed onto such a blackboard, is broadcast globally to multiple cognitive systems—which process these data to speak, store or call up a memory or execute an action.

Because the blackboard has limited space, we can only be aware of a little information at any given instant. The network of neurons that broadcast these messages is hypothesized to be located in the frontal and parietal lobes. Once these sparse data are broadcast on this network and are globally available, the information becomes conscious. That is, the subject becomes aware of it. Whereas current machines do not yet rise to this level of cognitive sophistication, this is only a question of time. GNW posits that computers of the future will be conscious.

Integrated information theory (IIT), developed by Tononi and his collaborators, including me, has a very different starting point: experience itself. Each experience has certain essential properties. It is intrinsic, existing only for the subject as its “owner”; it is structured (a yellow cab braking while a brown dog crosses the street); and it is specific—distinct from any other conscious experience, such as a particular frame in a movie. Furthermore, it is unified and definite. When you sit on a park bench on a warm, sunny day, watching children play, the different parts of the experience—the breeze playing in your hair or the joy of hearing your toddler laugh—cannot be separated into parts without the experience ceasing to be what it is.

Tononi postulates that any complex and interconnected mechanism whose structure encodes a set of cause-and-effect relationships will have these properties—and so will have some level of consciousness. It will feel like something from the inside. But if, like the cerebellum, the mechanism lacks integration and complexity, it will not be aware of anything. As IIT states it, consciousness is intrinsic causal power associated with complex mechanisms such as the human brain.

IIT theory also derives, from the complexity of the underlying interconnected structure, a single nonnegative number Φ (pronounced “fy”) that quantifies this consciousness. If Φ is zero, the system does not feel like anything to be itself. Conversely, the bigger this number, the more intrinsic causal power the system possesses and the more conscious it is. The brain, which has enormous and highly specific connectivity, possesses very high Φ, which implies a high level of consciousness. IIT explains a number of observations, such as why the cerebellum does not contribute to consciousness and why the zap-and-zip meter works. (The quantity the meter measures is a very crude approximation of Φ.)

IIT also predicts that a sophisticated simulation of a human brain running on a digital computer cannot be conscious—even if it can speak in a manner indistinguishable from a human being. Just as simulating the massive gravitational attraction of a black hole does not actually deform spacetime around the computer implementing the astrophysical code, programming for consciousness will never create a conscious computer. Consciousness cannot be computed: it must be built into the structure of the system.

Two challenges lie ahead. One is to use the increasingly refined tools at our disposal to observe and probe the vast coalitions of highly heterogeneous neurons making up the brain to further delineate the neuronal footprints of consciousness. This effort will take decades, given the byzantine complexity of the central nervous system. The other is to verify or falsify the two, currently dominant, theories. Or, perhaps, to construct a better theory out of fragments of these two that will satisfactorily explain the central puzzle of our existence: how a three-pound organ with the consistency of tofu exudes the feeling of life.

Epigenetic clock

From Wikipedia, the free encyclopedia

An epigenetic clock is a type of a molecular age estimation method based on DNA methylation levels. Pre-eminent examples for epigenetic clocks are Horvath's clock,^[1][2][3][4] which applies to all human tissues/cells, and Hannum's clock,^[5] which applies to blood.

History

The strong effects of age on DNA methylation levels have been known since the late 1960s.^[6] A vast literature describes sets of CpGs whose DNA methylation levels correlate with age, e.g.^{[7][8][9][10][11]} The first robust demonstration that DNA methylation levels in saliva could generate accurate age predictors was published by a UCLA team including Steve Horvath in 2011 (Bocklandt et al 2011).^[12] The labs of Trey Ideker and Kang Zhang at the University of California San Diego published the Hannum epigenetic clock (Hannum 2013),^[5] which consisted of 71 markers that accurately estimate age based on blood methylation levels. The first multi-tissue epigenetic clock, Horvath's epigenetic clock, was developed by Steve Horvath, a professor of human genetics and of biostatistics at UCLA (Horvath 2013).^[1][3] Horvath spent over 4 years collecting publicly available Illumina DNA methylation data and identifying suitable statistical methods.^[13] The personal story behind the discovery was featured in Nature.^[14] The age estimator was developed using 8,000 samples from 82 Illumina DNA methylation array datasets, encompassing 51 healthy tissues and cell types. The major innovation of Horvath's epigenetic clock lies in its wide applicability: the same set of 353 CpGs and the same prediction algorithm is used irrespective of the DNA source within the organism, i.e. it does not require any adjustments or offsets.^[1] This property allows one to compare the ages of different areas of the human body using the same aging clock.

Relationship to a cause of biological aging

It is not yet known what exactly is measured by DNA methylation age. Horvath hypothesized that DNA methylation age measures the cumulative effect of an epigenetic maintenance system but details are unknown. The fact that DNA methylation age of blood predicts all-cause mortality in later life ^{[15][16][17][18]} strongly suggests that it relates to a process that causes aging.^[19] However, it is unlikely that the 353 clock CpGs are special or play a direct causal role in the aging process.^[1] Rather, the epigenetic clock captures an emergent property of the epigenome.

Epigenetic clock theory of aging

Horvath and Raj ^[20] proposed an epigenetic clock theory of aging with the following tenets:

• Biological aging results as an unintended consequence of both developmental programs and maintenance program, the molecular footprints of which give rise to DNA methylation age estimators.
• The precise mechanisms linking the innate molecular processes (underlying DNAm age) to the decline in tissue function probably relate to both intracellular changes (leading to a loss of cellular identity) and subtle changes in cell composition, for example, fully functioning somatic stem cells.
• At the molecular level, DNAm age is a proximal readout of a collection of innate ageing processes that conspire with other, independent root causes of ageing to the detriment of tissue function

Motivation for biological clocks

In general, biological aging clocks and biomarkers of aging are expected to find many uses in biological research since age is a fundamental characteristic of most organisms. Accurate measures of biological age (biological aging clocks) could be useful for

• testing the validity of various theories of biological aging,
• diagnosing various age related diseases and for defining cancer subtypes,
• predicting/prognosticating the onset of various diseases,
• serving as surrogate markers for evaluating therapeutic interventions including rejuvenation approaches,
• studying developmental biology and cell differentiation,
• forensic applications, for example to estimate the age of a suspect based on blood left on a crime scene.

Overall, biological clocks are expected to be useful for studying what causes aging and what can be done against it.

Properties of Horvath's clock

The clock is defined as an age estimation method based on 353 epigenetic markers on the DNA. The 353 markers measure DNA methylation of CpG dinucleotides. Estimated age ("predicted age" in mathematical usage), also referred to as DNA methylation age, has the following properties: first, it is close to zero for embryonic and induced pluripotent stem cells; second, it correlates with cell passage number; third, it gives rise to a highly heritable measure of age acceleration; and, fourth, it is applicable to chimpanzee tissues (which are used as human analogs for biological testing purposes). Organismal growth (and concomitant cell division) leads to a high ticking rate of the epigenetic clock that slows down to a constant ticking rate (linear dependence) after adulthood (age 20).^[1] The fact that DNA methylation age of blood predicts all-cause mortality in later life even after adjusting for known risk factors ^[15][16] suggests that it relates to a process that causes aging. Similarly, markers of physical and mental fitness are associated with the epigenetic clock (lower abilities associated with age acceleration).^[21]

Salient features of Horvath's epigenetic clock include its high accuracy and its applicability to a broad spectrum of tissues and cell types. Since it allows one to contrast the ages of different tissues from the same subject, it can be used to identify tissues that show evidence of accelerated age due to disease.

Statistical approach

The basic approach is to form a weighted average of the 353 clock CpGs, which is then transformed to DNAm age using a calibration function. The calibration function reveals that the epigenetic clock has a high ticking rate until adulthood, after which it slows to a constant ticking rate. Using the training data sets, Horvath used a penalized regression model (Elastic net regularization) to regress a calibrated version of chronological age on 21,369 CpG probes that were present both on the Illumina 450K and 27K platform and had fewer than 10 missing values. DNAm age is defined as estimated ("predicted") age. The elastic net predictor automatically selected 353 CpGs. 193 of the 353 CpGs correlate positively with age while the remaining 160 CpGs correlate negatively with age. R software and a freely available web-based tool can be found at the following webpage.^[22]

Accuracy

The median error of estimated age is 3.6 years across a wide spectrum of tissues and cell types .^[1] The epigenetic clock performs well in heterogeneous tissues (for example, whole blood, peripheral blood mononuclear cells, cerebellar samples, occipital cortex, buccal epithelium, colon, adipose, kidney, liver, lung, saliva, uterine cervix, epidermis, muscle) as well as in individual cell types such as CD4 T cells, CD14 monocytes, glial cells, neurons, immortalized B cells, mesenchymal stromal cells.^[1] However, accuracy depends to some extent on the source of the DNA.

Comparison with other biological clocks

The epigenetic clock leads to a chronological age prediction that has a Pearson correlation coefficient of r=0.96 with chronological age (Figure 2 in ^[1]). Thus the age correlation is close to its maximum possible correlation value of 1. Other biological clocks are based on a) telomere length, b) p16INK4a expression levels (also known as INK4a/ARF locus),^[23] and c) microsatellite mutations.^[24] The correlation between chronological age and telomere length is r=−0.51 in women and r=−0.55 in men.^[25] The correlation between chronological age and expression levels of p16INK4a in T cells is r=0.56.^[26] p16INK4a expression levels only relate to age in T cells, a type of white blood cells.^{[citation needed]} The microsatellite clock measures not chronological age but age in terms of elapsed cell divisions within a tissue.^{[citation needed]}

Applications of Horvath's clock

By contrasting DNA methylation age (estimated age) with chronological age, one can define measures of age acceleration. Age acceleration can be defined as the difference between DNA methylation age and chronological age. Alternatively, it can be defined as the residual that results from regressing DNAm age on chronological age. The latter measure is attractive because it does not correlate with chronological age. A positive/negative value of epigenetic age acceleration suggests that the underlying tissue ages faster/slower than expected.

Genetic studies of epigenetic age acceleration

The broad sense heritability (defined via Falconer's formula) of age acceleration of blood from older subjects is around 40% but it appears to be much higher in newborns.^[1] Similarly, the age acceleration of brain tissue (prefrontal cortex) was found to be 41% in older subjects.^[27] Genome-wide association studies (GWAS) of epigenetic age acceleration in postmortem brain samples have identified several SNPs at a genomewide significance level.^[28][29] GWAS of age acceleration in blood have identified several genome-wide significant genetic loci including the telomerase reverse transcriptase gene (TERT) locus .^[30] Genetic variants associated with longer leukocyte telomere length in TERT gene paradoxically confer higher epigenetic age acceleration in blood.^[30]

Lifestyle factors

In general, lifestyle factors have only weak effects on epigenetic age acceleration in blood.^[31] However, cross sectional studies of extrinsic epigenetic aging rates in blood confirm the conventional wisdom regarding the benefits of education, eating a high plant diet with lean meats, moderate alcohol consumption, physical activity and the risks associated with metabolic syndrome.

Obesity and metabolic syndrome

The epigenetic clock was used to study the relationship between high body mass index (BMI) and the DNA methylation ages of human blood, liver, muscle and adipose tissue.^[32] A significant correlation (r=0.42) between BMI and epigenetic age acceleration could be observed for the liver. A much larger sample size (n=4200 blood samples) revealed a weak but statistically significant correlation (r=0.09) between BMI and intrinsic age acceleration of blood.^[31] The same large study found that various biomarkers of metabolic syndrome (glucose-, insulin-, triglyceride levels, C-reactive protein, waist-to-hip ratio) were associated with epigenetic age acceleration in blood.^[31] Conversely, high levels of the good cholesterol HDL were associated with a lower epigenetic aging rate of blood.^[31]

Female breast tissue is older than expected

DNAm age is higher than chronological age in female breast tissue that is adjacent to breast cancer tissue.^[1] Since normal tissue which is adjacent to other cancer types does not exhibit a similar age acceleration effect, this finding suggests that normal female breast tissue ages faster than other parts of the body.^[1] Similarly, normal breast tissue samples from women without cancer have been found to be substantially older than blood samples collected from the same women at the same time PMID 28364215.

Cancer tissue

Cancer tissues show both positive and negative age acceleration effects. For most tumor types, no significant relationship can be observed between age acceleration and tumor morphology (grade/stage).^[1][2] On average, cancer tissues with mutated TP53 have a lower age acceleration than those without it.^[1] Further, cancer tissues with high age acceleration tend to have fewer somatic mutations than those with low age acceleration.^[1][2] Age acceleration is highly related to various genomic aberrations in cancer tissues. Somatic mutations in estrogen receptors or progesterone receptors are associated with accelerated DNAm age in breast cancer.^[1] Colorectal cancer samples with a BRAF (V600E) mutation or promoter hypermethylation of the mismatch repair gene MLH1 are associated with an increased age acceleration.^[1] Age acceleration in glioblastoma multiforme samples is highly significantly associated with certain mutations in H3F3A.^[1] One study suggests that the epigenetic age of blood tissue may be prognostic of lung cancer incidence.^[33]

Trisomy 21 (Down syndrome)

Down Syndrome (DS) entails an increased risk of many chronic diseases that are typically associated with older age. The clinical manifestations of accelerated aging suggest that trisomy 21 increases the biological age of tissues, but molecular evidence for this hypothesis has been sparse. According to the epigenetic clock, trisomy 21 significantly increases the age of blood and brain tissue (on average by 6.6 years).^[34]

Alzheimer's disease related neuropathology

Epigenetic age acceleration of the human prefrontal cortex was found to be correlated with several neuropathological measurements that play a role in Alzheimer's disease ^[27] Further, it was found to be associated with a decline in global cognitive functioning, and memory functioning among individuals with Alzheimer's disease.^[27] The epigenetic age of blood relates to cognitive functioning in the elderly.^[21] Overall, these results strongly suggest that the epigenetic clock lends itself for measuring the biological age of the brain.

Cerebellum ages slowly

It has been difficult to identify tissues that seem to evade aging due to the lack of biomarkers of tissue age that allow one to contrast compare the ages of different tissues. An application of epigenetic clock to 30 anatomic sites from six centenarians and younger subjects revealed that the cerebellum ages slowly: it is about 15 years younger than expected in a centenarian.^[35] This finding might explain why the cerebellum exhibits fewer neuropathological hallmarks of age related dementias compared to other brain regions. In younger subjects (e.g. younger than 70), brain regions and brain cells appear to have roughly the same age.^[1][35] Several SNPs and genes have been identified that relate to the epigenetic age of the cerebellum ^[28]

Huntington's disease

Huntington's disease has been found to increase the epigenetic aging rates of several human brain regions.^[36]

Centenarians age slowly

The offspring of semi-supercentenarians (subjects who reached an age of 105–109 years) have a lower epigenetic age than age-matched controls (age difference=5.1 years in blood) and centenarians are younger (8.6 years) than expected based on their chronological age.^[18]

HIV infection

Infection with the Human Immunodeficiency Virus-1 (HIV) is associated with clinical symptoms of accelerated aging, as evidenced by increased incidence and diversity of age-related illnesses at relatively young ages. But it has been difficult to detect an accelerated aging effect on a molecular level. An epigenetic clock analysis of human DNA from HIV+ subjects and controls detected a significant age acceleration effect in brain (7.4 years) and blood (5.2 years) tissue due to HIV-1 infection.^[37] These results are consistent with an independent study that also found an age advancement of 5 years in blood of HIV patients and a strong effect of the HLA locus.^[38]

Parkinson's disease

A large-scale study suggests that the blood of Parkinson's disease subjects exhibits (relatively weak) accelerated aging effects.^[39]

Developmental disorder: syndrome X

Children with a very rare disorder known as syndrome X maintain the façade of persistent toddler-like features while aging from birth to adulthood. Since the physical development of these children is dramatically delayed, these children appear to be a toddler or at best a preschooler. According to an epigenetic clock analysis, blood tissue from syndrome X cases is not younger than expected.^[40]

Menopause accelerates epigenetic aging

The following results strongly suggest that the loss of female hormones resulting from menopause accelerates the epigenetic aging rate of blood and possibly that of other tissues.^[41] First, early menopause has been found to be associated with an increased epigenetic age acceleration of blood.^[41] Second, surgical menopause (due to bilateral oophorectomy) is associated with epigenetic age acceleration in blood and saliva. Third, menopausal hormone therapy, which mitigates hormonal loss, is associated with a negative age acceleration of buccal cells (but not of blood cells).^[41] Fourth, genetic markers that are associated with early menopause are also associated with increased epigenetic age acceleration in blood.^[41]

Cellular senescence versus epigenetic aging

A confounding aspect of biological aging is the nature and role of senescent cells. It is unclear whether the three major types of cellular senescence, namely replicative senescence, oncogene-induced senescence and DNA damage-induced senescence are descriptions of the same phenomenon instigated by different sources, or if each of these is distinct, and how they are associated with epigenetic aging. Induction of replicative senescence (RS) and oncogene-induced senescence (OIS) were found to be accompanied by epigenetic aging of primary cells but senescence induced by DNA damage was not, even though RS and OIS activate the cellular DNA damage response pathway.^[42] These results highlight the independence of cellular senescence from epigenetic aging. Consistent with this, telomerase-immortalised cells continued to age (according to the epigenetic clock) without having been treated with any senescence inducers or DNA-damaging agents, re-affirming the independence of the process of epigenetic ageing from telomeres, cellular senescence, and the DNA damage response pathway. Although the uncoupling of senescence from cellular aging appears at first sight to be inconsistent with the fact that senescent cells contribute to the physical manifestation of organism ageing, as demonstrated by Baker et al., where removal of senescent cells slowed down aging.^[43] However, the epigenetic clock analysis of senescence suggests that cellular senescence is a state that cells are forced into as a result of external pressures such as DNA damage, ectopic oncogene expression and exhaustive proliferation of cells to replenish those eliminated by external/environmental factors.^[42] These senescent cells, in sufficient numbers, will probably cause the deterioration of tissues, which is interpreted as organism ageing. However, at the cellular level, aging, as measured by the epigenetic clock, is distinct from senescence. It is an intrinsic mechanism that exists from the birth of the cell and continues. This implies that if cells are not shunted into senescence by the external pressures described above, they would still continue to age. This is consistent with the fact that mice with naturally long telomeres still age and eventually die even though their telomere lengths are far longer than the critical limit, and they age prematurely when their telomeres are forcibly shortened, due to replicative senescence. Therefore, cellular senescence is a route by which cells exit prematurely from the natural course of cellular aging.^[42]

Effect of sex and race/ethnicity

Men age faster than women according to epigenetic age acceleration in blood, brain, saliva, and many other tissues. ^[44] The epignetic clock method applies to all examined racial/ethnic groups in the sense that DNAm age is highly correlated with chronological age. But ethnicity can be associated with epigenetic age acceleration.^[44] For example, the blood of Hispanics and the Tsimané ages more slowly than that of other populations which might explain the Hispanic mortality paradox.^[44]

Rejuvenation effect due to stem cell transplantation in blood

Hematopoietic stem cell transplantation, which transplants these cells from a young donor to an older recipient, rejuvenates the epigenetic age of blood to that of the donor PMID 28550187. However, graft-versus-host disease is associated with increased DNA methyhlation age PMID 28550187.

Progeria

Adult progeria also known as Werner syndrome is associated with epigenetic age acceleration in blood.^[45]

Biological mechanism behind the epigenetic clock

Despite the fact that biomarkers of ageing based on DNA methylation data have enabled accurate age estimates for any tissue across the entire life course, the precise biological mechanism behind the epigenetic clock is currently unknown.^[20] However, epigenetic biomarkers may help to address long-standing questions in many fields, including the central question: why do we age? The following explanations have been proposed in the literature.

Possible explanation 1: Epigenomic maintenance system

Horvath hypothesized that his clock arises from a methylation footprint left by an epigenomic maintenance system.^[1]

Possible explanation 2: Unrepaired DNA damages

Endogenous DNA damages occur frequently including about 50 double-strand DNA breaks per cell cycle^[46] and about 10,000 oxidative damages per day (see DNA damage (naturally occurring)). During repair of double-strand breaks many epigenetic alterations are introduced, and in a percentage of cases epigenetic alterations remain after repair is completed, including increased methylation of CpG island promoters.^[47][48][49] Similar, but usually transient epigenetic alterations were recently found during repair of oxidative damages caused by H₂O₂, and it was suggested that occasionally these epigenetic alterations may also remain after repair.^[50] These accumulated epigenetic alterations may contribute to the epigenetic clock. Accumulation of epigenetic alterations may parallel the accumulation of un-repaired DNA damages that are proposed to cause aging (see DNA damage theory of aging).

Other age estimators based on DNA methylation levels

Several other age estimators have been described in the literature.

1) Weidner et al. (2014) describe an age estimator for DNA from blood that uses only three CpG sites of genes hardly affected by aging (cg25809905 in integrin, alpha 2b (ITGA2B); cg02228185 in aspartoacylase (ASPA) and cg17861230 in phosphodiesterase 4C, cAMP specific (PDE4C)).^[51] The age estimator by Weidener et al. (2014) applies only to blood. Even in blood this sparse estimator is far less accurate than Horvath's epigenetic clock (Horvath 2014) when applied to data generated by the Illumina 27K or 450K platforms. ^[52] But the sparse estimator was developed for pyrosequencing data and is highly cost effective. ^[53]

2) Hannum et al. (2013) ^[5] report several age estimators: one for each tissue type. Each of these estimators requires covariate information (e.g. gender, body mass index, batch). The authors mention that each tissue led to a clear linear offset (intercept and slope). Therefore, the authors had to adjust the blood-based age estimator for each tissue type using a linear model. When the Hannum estimator is applied to other tissues, it leads to a high error (due to poor calibration) as can be seen from Figure 4A in Hannum et al. (2013). Hannum et al. adjusted their blood-based age estimator (by adjusting the slope and the intercept term) in order to apply it to other tissue types. Since this adjustment step removes differences between tissue, the blood-based estimator from Hannum et al. cannot be used to compare the ages of different tissues/organs. In contrast, a salient characteristic of the epigenetic clock is that one does not have to carry out such a calibration step:^[1] it always uses the same CpGs and the same coefficient values. Therefore, Horvath's epigenetic clock can be used to compare the ages of different tissues/cells/organs from the same individual. While the age estimators from Hannum et al. cannot be used to compare the ages of different normal tissues, they can be used to compare the age of a cancerous tissue with that of a corresponding normal (non-cancerous) tissue. Hannum et al. reported pronounced age acceleration effects in all cancers. In contrast, Horvath's epigenetic clock ^[2][54] reveals that some cancer types (e.g. triple negative breast cancers or uterine corpus endometrial carcinoma) exhibit negative age acceleration, i.e. cancer tissue can be much younger than expected. An important difference relates to additional covariates. Hannum's age estimators make use of covariates such as gender, body mass index, diabetes status, ethnicity, and batch. Since new data involve different batches, one cannot apply it directly to new data. However, the authors present coefficient values for their CpGs in Supplementary Tables which can be used to define an aggregate measure that tends to be strongly correlated with chronological age but may be poorly calibrated (i.e. lead to high errors).

3.) Giuliani et al. identify genomic regions whose DNA methylation level correlates with age in human teeth. They propose the evaluation of DNA methylation at ELOVL2, FHL2, and PENK genes in DNA recovered from both cementum and pulp of the same modern teeth.^[57] They wish to apply this method also to historical and relatively ancient human teeth.

In a multicenter benchmarking study 18 research groups from three continents compared all promising methods for analyzing DNA methylation in the clinic and identified the most accurate methods, having concluded that epigenetic tests based on DNA methylation are a mature technology ready for broad clinical use.^[58]

Other species

Wang et al., (in mice livers)^[59] and Petkovich et al.,(based on mice blood DNA methylation profiles)^[60] examined whether mice and humans experience similar patterns of change in the methylome with age. They found that mice treated with lifespan-extending interventions (surch as calorie restriction or dietary rapamycin) were significantly younger in epigenetic age than their untreated, wild-type age-matched controls. Mice age predictors also detects the longevity effects of gene knockouts, and rejuvenation of fibroblast-derived iPSCs.

Mice multi-tissue age predictor based on DNA methylation at 329 unique CpG sites reached a median absolute error of less than 4 weeks (~5% of lifespan). An attempt to use the human clock sites in mouse for age predictions showed that human clock is not fully conserved in mouse.^[61] Differences between human and mouse clocks suggests that epigenetic clocks need to be trained specifically for different species.^[62]

Changes to DNA methylation patterns have great potential for age estimation and biomarker search in domestic and wild animals.^[63]

Aging brain

From Wikipedia, the free encyclopedia

Age is a major risk factor for most common neurodegenerative diseases, including mild cognitive impairment, Alzheimer's disease, cerebrovascular disease, Parkinson's disease and Lou Gehrig's disease. While much research has focused on diseases of aging, there are few informative studies on the molecular biology of the aging brain (usually spelled ageing brain in British English) in the absence of neurodegenerative disease or the neuropsychological profile of healthy older adults. However, research does suggest that the aging process is associated with several structural, chemical, and functional changes in the brain as well as a host of neurocognitive changes. Recent reports in model organisms suggest that as organisms age, there are distinct changes in the expression of genes at the single neuron level.^[1] This page is devoted to reviewing the changes associated with healthy aging.

Structural changes

Aging entails many physical, biological, chemical, and psychological changes. Therefore, it is logical to assume the brain is no exception to this phenomenon. CT scans have found that the cerebral ventricles expand as a function of age. More recent MRI studies have reported age-related regional decreases in cerebral volume.^[2][3] Regional volume reduction is not uniform; some brain regions shrink at a rate of up to 1% per year, whereas others remain relatively stable until the end of the life-span.^[4] The brain is very complex, and is composed of many different areas and types of tissue, or matter. The different functions of different tissues in the brain may be more or less susceptible to age-induced changes.^[2] The brain matter can be broadly classified as either grey matter, or white matter. Grey matter consists of cell bodies in the cortex and subcortical nuclei, whereas white matter consists of tightly packed myelinated axons connecting the neurons of the cerebral cortex to each other and with the periphery.^[2]

Loss of neural circuits and brain plasticity

Brain plasticity refers to the brain's ability to change structure and function.^[5][6] This ties into the common phrase, "if you don't use it, you lose it," which is another way of saying, if you don't use it, your brain will devote less somatotopic space for it. One proposed mechanism for the observed age-related plasticity deficits in animals is the result of age-induced alterations in calcium regulation.^[7] The changes in our abilities to handle calcium will ultimately influence neuronal firing and the ability to propagate action potentials, which in turn would affect the ability of the brain to alter its structure or function (i.e. its plastic nature). Due to the complexity of the brain, with all of its structures and functions, it is logical to assume that some areas would be more vulnerable to aging than others. Two circuits worth mentioning here are the hippocampal and neocortical circuits.^[8] It has been suggested that age-related cognitive decline is due in part not to neuronal death but to synaptic alterations. Evidence in support of this idea from animal work has also suggested that this cognitive deficit is due to functional and biochemical factors such as changes in enzymatic activity, chemical messengers, or gene expression in cortical circuits.^[8]

Thinning of the cortex

Advances in MRI technology have provided the ability to see the brain structure in great detail in an easy, non-invasive manner in vivo.^[9] Bartzokis et al., has noted that there is a decrease in grey matter volume between adulthood and old age, whereas white matter volume was found to increase from age 19-40, and decline after this age.^[9] Studies using Voxel-based morphometry have identified areas such as the insula and superior parietal gyri as being especially vulnerable to age-related losses in grey matter of older adults.^[9] Sowell et al., reported that the first 6 decades of an individual's life were correlated with the most rapid decreases in grey matter density, and this occurred over dorsal, frontal, and parietal lobes on both interhemispheric and lateral brain surfaces. It is also worth noting that areas such as the cingulate gyrus, and occipital cortex surrounding the calcarine sulcus appear exempt from this decrease in grey matter density over time.^[9] Age effects on grey matter density in the posterior temporal cortex appear more predominantly in the left versus right hemisphere, and were confined to posterior language cortices. Certain language functions such as word retrieval and production were found to be located to more anterior language cortices, and deteriorate as a function of age. Sowell et al., also reported that these anterior language cortices were found to mature and decline earlier than the more posterior language cortices.^[9] It has also been found that the width of sulcus not only increases with age,^[10] but also with cognitive decline in the elderly.^[11]

Age-related neuronal morphology

There is converging evidence from cognitive neuroscientists around the world that age-induced cognitive deficits may not be due to neuronal loss or cell death, but rather may be the result of small region-specific changes to the morphology of neurons.^[7] Studies by Duan et al., have shown that dendritic arbors and dendritic spines of cortical pyramidal neurons decrease in size and/or number in specific regions and layers of human and non-human primate cortex as a result of age (Duan et al., 2003; morph). Interestingly, a 46% decrease in spine number and spine density has been reported in humans older than 50 compared with younger individuals.^[8] An electron microscopy study in monkeys reported a 50% loss in spines on the apical dendritic tufts of pyramidal cells in prefrontal cortex of old animals (27–32 years old) compared with young ones (6–9 years old).^[8]

Neurofibrillary tangles

Age-related neuro-pathologies such as Alzheimer's disease, Parkinson's disease, diabetes, hypertension and arteriosclerosis make it difficult to distinguish the normal patterns of aging.^[12][13] One of the important differences between normal aging and pathological aging is the location of neurofibrillary tangles. Neurofibrillary tangles are composed of paired helical filaments (PHF).^[14] In normal, non-demented aging, the number of tangles in each affected cell body is relatively low^[14] and restricted to the olfactory nucleus, parahippocampal gyrus, amygdala and entorhinal cortex.^[15] As the non-demented individual ages, there is a general increase in the density of tangles, but no significant difference in where tangles are found.^[15] The other main neurodegenerative contributor commonly found in the brain of patients with AD is amyloid plaques. However, unlike tangles, plaques have not been found to be a consistent feature of normal aging.^[15]

Role of oxidative stress

Cognitive impairment has been attributed to oxidative stress, inflammatory reactions and changes in the cerebral microvasculature.^[16] The exact impact of each of these mechanisms in affecting cognitive aging is unknown. Oxidative stress is the most controllable risk factor and is the best understood. The online Merriam-Webster Medical Dictionary defines oxidative stress as, "physiological stress on the body that is caused by the cumulative damage done by free radicals inadequately neutralized by antioxidants and that is to be associated with aging."^[17] Hence oxidative stress is the damage done to the cells by free radicals that have been released from the oxidation process.

Compared to other tissues in the body, the brain is deemed unusually sensitive to oxidative damage.^[18] Increased oxidative damage has been associated with neurodegenerative diseases, mild cognitive impairment and individual differences in cognition in healthy elderly people. In 'normal aging', the brain is undergoing oxidative stress in a multitude of ways. The main contributors include protein oxidation, lipid peroxidation and oxidative modifications in nuclear and mitochondrial DNA.^[18] Oxidative stress can damage DNA replication and inhibit repair through many complex processes, including telomere shortening in DNA components.^[19] Each time a somatic cell replicates, the telomeric DNA component shortens. As telomere length is partly inheritable,^[19] there are individual differences in the age of onset of cognitive decline.

DNA damage

At least 25 studies have demonstrated that DNA damage accumulates with age in the mammalian brain. This DNA damage includes the oxidized nucleoside 8-hydroxydeoxyguanosine (8-OHdG), single- and double-strand breaks, DNA-protein crosslinks and malondialdehyde adducts (reviewed in Bernstein et al.^[20]). Increasing DNA damage with age has been reported in the brains of the mouse, rat, gerbil, rabbit, dog, and human. Young 4-day-old rats have about 3,000 single-strand breaks and 156 double-strand breaks per neuron, whereas in rats older than 2 years the level of damage increases to about 7,400 single-strand breaks and 600 double-strand breaks per neuron.^[21]

Lu et al.^[22] studied the transcriptional profiles of the human frontal cortex of individuals ranging from 26 to 106 years of age. This led to the identification of a set of genes whose expression was altered after age 40. They further found that the promoter sequences of these particular genes accumulated oxidative DNA damage, including 8-OHdG, with age (see DNA damage theory of aging). They concluded that DNA damage may reduce the expression of selectively vulnerable genes involved in learning, memory and neuronal survival, initiating a pattern of brain aging that starts early in life.

Chemical changes

In addition to the structural changes that the brain incurs with age, the aging process also entails a broad range of biochemical changes. More specifically, neurons communicate with each other via specialized chemical messengers called neurotransmitters. Several studies have identified a number of these neurotransmitters, as well as their receptors, that exhibit a marked alteration in different regions of the brain as part of the normal aging process.

Dopamine

An overwhelming number of studies have reported age-related changes in dopamine synthesis, binding sites, and number of receptors. Studies using positron emission tomography (PET) in living human subjects have shown a significant age-related decline in dopamine synthesis,^[23] notably in the striatum and extrastriatal regions (excluding the midbrain).^[24] Significant age-related decreases in dopamine receptors D₁, D₂, and D₃ have also been highly reported.^{[25][26][27][28][29]} A general decrease in D₁ and D₂ receptors has been shown,^[27] and more specifically a decrease of D₁ and D₂ receptor binding in the caudate nucleus and putamen.^[26][29] A general decrease in D₁ receptor density has also been shown to occur with age. Significant age-related declines in dopamine receptors, D₂ and D₃ were detected in the anterior cingulate cortex, frontal cortex, lateral temporal cortex, hippocampus, medial temporal cortex, amygdala, medial thalamus, and lateral thalamus^[25] One study also indicated a significant inverse correlation between dopamine binding in the occipital cortex and age.^[26] Postmortem studies also show that the number of D₁ and D₂ receptors decline with age in both the caudate nucleus and the putamen, although the ratio of these receptors did not show age-related changes.^[28] The loss of dopamine with age is thought to be responsible for many neurological symptoms that increase in frequency with age, such as decreased arm swing and increased rigidity.^[30] Changes in dopamine levels may also cause age-related changes in cognitive flexibility.^[30]

Serotonin

Decreasing levels of different serotonin receptors and the serotonin transporter, 5-HTT, have also been shown to occur with age. Studies conducted using PET methods on humans, in vivo, show that levels of the 5-HT₂ receptor in the caudate nucleus, putamen, and frontal cerebral cortex, decline with age.^[29] A decreased binding capacity of the 5-HT₂ receptor in the frontal cortex was also found,^[27] as well as a decreased binding capacity of the serotonin transporter, 5-HHT, in the thalamus and the midbrain.^[31] Postmortem studies on humans have indicated decreased binding capacities of serotonin and a decrease in the number of S₁ receptors in the frontal cortex and hippocampus as well as a decrease in affinity in the putamen.^[32]

Glutamate

Glutamate is another neurotransmitter that tends to decrease with age.^[33][34][35] Studies have shown older subjects to have lower glutamate concentration in the motor cortex compared to younger subjects^[35] A significant age-related decline especially in the parietal gray matter, basal ganglia, and to a lesser degree, the frontal white matter, has also been noted.^[33][34] Although these levels were studied in the normal human brain, the parietal and basal ganglia regions are often affected in degenerative brain diseases associated with aging and it has therefore been suggested that brain glutamate may be useful as a marker of brain diseases that are affected by aging.^[33]

Neuropsychological changes

Changes in orientation

Orientation is defined as the awareness of self in relation to one's surroundings^[36] Often orientation is examined by distinguishing whether a person has a sense of time, place, and person. Deficits in orientation are one of the most common symptoms of brain disease, hence tests of orientation are included in almost all medical and neuropsychological evaluations.^[37] While research has primarily focused on levels of orientation among clinical populations, a small number of studies have examined whether there is a normal decline in orientation among healthy aging adults. Results have been somewhat inconclusive. Some studies suggest that orientation does not decline over the lifespan.^[38][39] For example, in one study 92% of normal elderly adults (65–84 years) presented with perfect or near perfect orientation.^[40] However some data suggest that mild changes in orientation may be a normal part of aging.^[41][42] For example, Sweet and colleagues concluded that "older persons with normal, healthy memory may have mild orientation difficulties. In contrast, younger people with normal memory have virtually no orientation problems"^[42] (p. 505). So although current research suggests that normal aging is not usually associated with significant declines in orientation, mild difficulties may be a part of normal aging and not necessarily a sign of pathology.

Changes in attention

Many older adults notice a decline in their attentional abilities.^[43] Attention is a broad construct that refers to "the cognitive ability that allows us to deal with the inherent processing limitations of the human brain by selecting information for further processing" (p. 334).^[44] Since the human brain has limited resources, people use their attention to zone in on specific stimuli and block out others.

If older adults have fewer attentional resources than younger adults, we would expect that when two tasks must be carried out at the same time, older adults' performance will decline more than that of younger adults. However, a large review of studies on cognition and aging suggest that this hypothesis has not been wholly supported.^[45] While some studies have found that older adults have a more difficult time encoding and retrieving information when their attention is divided, other studies have not found meaningful differences from younger adults. Similarly, one might expect older adults to do poorly on tasks of sustained attention, which measure the ability to attend to and respond to stimuli for an extended period of time. However, studies suggest that sustained attention shows no decline with age. Results suggest that sustained attention increases in early adulthood and then remains relatively stable, at least through the seventh decade of life.^[46] More research is needed on how normal aging impacts attention after age eighty.

It is worth noting that there are factors other than true attentional abilities that might relate to difficulty paying attention. For example, it is possible that sensory deficits impact older adults' attentional abilities. In other words, impaired hearing or vision may make it more difficult for older adults to do well on tasks of visual and verbal attention.^[43]

Changes in memory

Many different types of memory have been identified in humans, such as declarative memory (including episodic memory and semantic memory), working memory, spatial memory, and procedural memory.^[2] Studies done, have found that memory functions, more specifically those associated with the medial temporal lobe are especially vulnerable to age-related decline.^[8] A number of studies utilizing a variety of methods such as histological, structural imaging, functional imaging, and receptor binding have supplied converging evidence that the frontal lobes and frontal-striatal dopaminergic pathways are especially affected by age-related processes resulting in memory changes.^[2]

Changes in language

Changes in performance on verbal tasks, as well as the location, extent, and signal intensity of BOLD signal changes measured with functional MRI, vary in predictable patterns with age. For example, behavioral changes associated with age include compromised performance on tasks related to word retrieval, comprehension of sentences with high syntactic and/or working memory demands, and production of such sentences.^[47]

Genetic changes

Variation in the effects of aging among individuals can be attributed to both genetic and environmental factors. As in so many other science disciplines, the nature and nurture debate is an ongoing conflict in the field of cognitive neuroscience.^[13][14] The search for genetic factors has always been an important aspect in trying to understand neuro-pathological processes. Research focused on discovering the genetic component in developing AD has also contributed greatly to the understanding the genetics behind normal or "non-pathological" aging.^[14]

The human brain shows a decline in function and a change in gene expression. This modulation in gene expression may be due to oxidative DNA damage at promoter regions in the genome.^[22] Genes that are down-regulated over the age of 40 include:

• GluR1 AMPA receptor subunit
• NMDA R2A receptor subunit (involved in learning)
• Subunits of the GABA-A receptor
• Genes involved in long-term potentiation e.g. calmodulin 1 and CAM kinase II alpha.
• Calcium signaling genes
• Synaptic plasticity genes
• Synaptic vesicle release and recycling genes

Genes that are upregulated include:

• Genes associated with stress response and DNA repair
• Antioxidant defence

Epigenetic age analysis of different brain regions

The cerebellum is the youngest brain region (and probably body part) in centenarians according to an epigenetic biomarker of tissue age known as epigenetic clock: it is about 15 years younger than expected in a centenarian.^[48] By contrast, all brain regions and brain cells appear to have roughly the same epigenetic age in subjects who are younger than 80.^[48][49] These findings suggest that the cerebellum is protected from aging effects, which in turn could explain why the cerebellum exhibits fewer neuropathological hallmarks of age related dementias compared to other brain regions.

Delaying the effects of aging

The process of aging may be inevitable; however, one many potentially delay the effects and severity of this progression. While there is no consensus of efficacy, the following are reported as delaying cognitive decline:

• High level of education^[14][50]
• Physical exercise^[51]
• Staying intellectually engaged, i.e. reading and mental activities (such as crossword puzzles)^[52]
• Maintaining social and friendship networks^[53]
• Maintaining a healthy diet, including omega-3 fatty acids, and protective antioxidants.^[13]

"Super Agers"

Longitudinal research studies have recently conducted genetic analyses of centenarians and their offspring to identify biomarkers as protective factors against the negative effects of aging. In particular, the cholesteryl ester transfer protein (CETP) gene is linked to prevention of cognitive decline and Alzheimer's disease.^[54] Specifically, valine CETP homozygotes but not heterozygotes experienced a relative 51% less decline in memory compared to a reference group after adjusting for demographic factors and APOE status.

Cognitive reserve

The ability of an individual to demonstrate no cognitive signs of aging despite an aging brain is called cognitive reserve.^[16][50] This hypothesis suggests that two patients might have the same brain pathology, with one person experiencing noticeable clinical symptoms, while the other continues to function relatively normally. Studies of cognitive reserve explore the specific biological, genetic and environmental differences which make one person susceptible to cognitive decline, and allow another to age more gracefully.

Nun Study

A study funded by the National Institute of Aging followed a group of 678 Roman Catholic sisters and recorded the effects of aging. The researchers used autobiographical essays collected as the nuns joined their Sisterhood. Findings suggest that early idea density, defined by number of ideas expressed and use of complex prepositions in these essays, was a significant predictor of lower risk for developing Alzheimer's disease in old age. Lower idea density was found to be significantly associated with lower brain weight, higher brain atrophy, and more neurofibrillary tangles.^[55]

Hypothalamus inflammation and GnRH

In a recent study (published May 1, 2013), it is suggested that the inflammation of the hypothalamus may be connected to our overall aging bodies. They focused on the activation of the protein complex NF-κB in mice test subjects, which showed increased activation as mice test subjects aged in the study. This activation not only affects aging, but affects a hormone known as GnRH, which has shown new anti-aging properties when injected into mice outside the hypothalamus, while causing the opposite effect when injected into the hypothalamus. It'll be some time before this can be applied to humans in a meaningful way, as more studies on this pathway are necessary to understand the mechanics of GnRH's anti-aging properties.^[56]