3807The Role of Circadian Biology in Health and Disease CHAPTER 485

SLEEP

FASTING

WAKE

FEEDING

Pancreas

Glucagon secretion

Muscle

Oxidative metabolism

CNS

Inhibition of hunger

Melatonin and GH secretion

Neurotoxic substance clearance

Memory consolidation

Lipid catabolism

Leptin secretion

Adipose

Liver

Gluconeogenesis

Glycogenolysis

Mitochondrial biogenesis

Cholesterol synthesis

Pancreas

Insulin secretion

Muscle

Fatty acid uptake

Glycolytic metabolism

Lipogenesis

Adiponectin production

Adipose

Liver

Glycogen synthesis

Bile acid synthesis

CNS

Hunger signals

Foraging behavior

Cortisol secretion

Neuronal activity

FIGURE 485-3 The circadian clock partitions behavioral, physiologic, and metabolic processes according to time of day. The partitioning of metabolic processes to

appropriate times of day is critical for the maintenance of health from cellular to mammalian organisms. This figure highlights which processes peak within the central

nervous system (CNS), muscle, adipose, liver, and pancreas during either the sleep/fasting or wake/feeding cycle in humans. GH, growth hormone.

Muscle

Insulin resistance

Sarcopenia

Vasculature

Thromboembolic events

Hypertension

Increased circulation of

inflammatory cytokines

Obesity

Adipose

Circadian

desynchrony

Circadian rhythm

sleep disorders

Jet lag

Shiftwork Advanced/delayed sleep disorder

CNS

Depression

Cognitive decline

Fibroblasts

Tumorigenesis

Hematopoetic

Autoimmunity

Pancreas

Hypoinsulinemia

Steatorrhea

IBD flare

Circadian dysbiosis

Intestine

Liver

Dyslipidemia

Steatosis

Metabolic syndrome

Adrenals

Chronic stress

Disrupted HPA axis

FIGURE 485-4 Pathologies resulting from circadian desynchrony. Circadian rhythm sleep disorders, including advanced/delayed sleep phase disorder, jet lag, social

jet lag, and shift work, result in a desynchrony between the environmental light-dark cycle “time” and the endogenous clock “time.” Pathologies can thus arise through

misalignment imposed by exogenous (e.g., altered light cycle and/or feeding rhythm) and endogenous factors (e.g., mutations in core clock genes). Such desynchrony results

in a host of wide-ranging pathologies across multiple tissues, including hypoinsulinemia (pancreas), disrupted hypothalamic-pituitary-adrenal (HPA) axis, autoimmunity,

hypertension, obesity, and metabolic syndrome. CNS, central nervous system; IBD, inflammatory bowel disease.

3808 PART 20 Frontiers

feedback loop known as the HPA axis. Hypothalamic secretion of

CRH and AVP promotes secretion of pituitary ACTH, which in turn

regulates rhythmic cortisol secretion from the adrenal cortex. Cortisol

release increases toward the morning, and this increase is believed

to prepare the brain and peripheral tissues for daytime activity and

food intake. AVP secretion in mice occurs prior to sleep to promote

water intake, thereby preventing dehydration during the sleep period.

Several hormonal systems are in fact influenced more by sleep than by

circadian rhythms. For instance, secretion of growth hormone (GH) is

profoundly blunted during acute overnight wakefulness. The secretion

of this hormone is primarily dependent on the occurrence of slowwave sleep, which is a homeostatically driven sleep stage that occurs

primarily in the first part of the sleep period. Cortisol also exhibits a

peak close after wakefulness: the cortisol awakening response (CAR).

This peak seems to be independent of a circadian rhythm, as the CAR

is severely blunted by acute overnight wakefulness, and both the CAR

and daytime cortisol are modulated by light exposure levels. Curtailed

sleep and overnight wakefulness increase the activity of the HPA axis

and may increase diurnal cortisol levels. Sleep also influences melatonin amplitude, such that sleep deprivation can increase melatonin

levels.

In working environments, the effects of curtailed sleep are often

confounded by mistimed exposure to light. Even low levels of light are

able to potently suppress melatonin secretion. Together with altered

timing in light exposure, perturbed hormonal levels likely represent

a mechanism through which altered timing and duration of sleep can

impact central and peripheral circadian oscillators.

Centrally-controlled rhythms of melatonin and cortisol are considered key regulators of extra-SCN and peripheral oscillators. Glucocorticoid receptors exist in both the central nervous system and in

peripheral tissues such as skeletal muscle, liver, and adipose tissue.

Upon acute shifts in light-dark or feeding cycles, rhythmic levels in

cortisol appear to modulate the rate at which behavioral and physiological rhythms are able to phase shift. Indeed, glucocorticoids

regulate clock gene expression in muscle, kidney, and lung, while the

powerful synthetic glucocorticoid dexamethasone is often employed

in vitro for its property to synchronize (e.g., reset) circadian rhythms

of cells, including liver cells. Consistent with a role for glucocorticoid

regulation of the clock, both adrenalectomy, which results in a lack of

cortisol, and exogenous corticosteroid supplementation significantly

disrupt the circadian clock system.

Several peripherally-produced hormones and peptides are not

only produced rhythmically but can also feed back to central clocks,

including the SCN. For instance, both cortisol and thyroid hormones

regulate their own rhythmic synthesis by feedback to central brain

regions, i.e., the hypothalamus (for cortisol) and pituitary (for both

hormones). Several other peripherally-produced factors have been

proposed to influence the central clock, including fatty acids produced

by the adipose tissue and fibroblast growth factor 21, a hormone primarily produced by the liver. Peripheral hormones that signal energy

state and hunger also exhibit circadian rhythms that seem to be regulated by local tissue clocks. The most extensively studied hormones are

leptin, which is released from white adipose tissue cells, and ghrelin,

which is released from specific endocrine cells in the upper fundus

region of the stomach. Ghrelin also exhibits significant peaks related

to anticipated meal timing, which persist for several days of fasting in

humans. Circulating rhythms of leptin and ghrelin are disrupted in

circadian mutant mice and are also perturbed in humans subjected

to circadian misalignment, with evidence for sex-specific effects. For

instance, Per and Cry mutant mice exhibit severely blunted leptin

rhythms, and wild-type mice exposed to jetlag (through repeatedly

altered light-dark cycles) show a reduced wake-associated decrease

in leptin. Similarly, humans forced to live 28-h days exhibit increased

24-h profiles of ghrelin and conversely decreased levels of leptin.

Ghrelin and leptin signal to several regions of the brain, including

integrative appetitive regions of the hypothalamus such as the arcuate

and paraventricular region. The response to these hormones is rhythmically regulated by the molecular clock within several such central

sites, effectively gating how these hormones influence rhythms of

food intake and energy homeostasis in a time-of-day– and nutrientdependent manner.

Role for the Clock in Metabolic Homeostasis Circadian control of glucose homeostasis has long been recognized, as early studies

demonstrated variation in glucose tolerance and insulin action across

the day. For example, due to a combination of circadian control of both

peripheral insulin sensitivity and pancreatic β-cell insulin secretion,

oral glucose tolerance is lower in the evening and afternoon compared

with the morning. Another example is the “dawn phenomenon,”

whereby glucose levels peak prior to the onset of activity. Further,

destruction of the SCN has been shown to abolish circadian regulation

of glucose metabolism in rats, and daily cycles of insulin secretion and

glucose tolerance are often perturbed in patients with type 2 diabetes,

who also exhibit changes in gene expression rhythms in peripheral

tissues such as adipose tissue. Changes in rhythmic parameters such

as insulin secretion have also been observed in first-degree relatives

of patients with type 2 diabetes, possibly highlighting a key hereditary

role for the circadian clock in the pathogenesis of metabolic disease.

Ablating clock genes in mice has revealed a key function for both

central and peripheral clocks in regulating energy homeostasis. The

circadian system has been shown to regulate rhythmic insulin secretion from the pancreas via both neural signals and hormonal levels

(e.g., cortisol and norepinephrine), as well as via cell-autonomous

clock regulation within the pancreatic β cell itself. An early observation was that whole-body Clock∆19/∆19 mutant mice developed obesity

without displaying hyperinsulinemia, a phenomenon that indicated

concurrent β-cell failure. This was later confirmed using pancreasand β-cell–specific Bmal1-deficient mice, which exhibited glucose

intolerance, hypoinsulinemia, and impaired glucose-stimulated insulin

secretion. The molecular clock within other peripheral tissues such as

liver, adipose tissue, and skeletal muscle also regulates circadian fluctuations in insulin sensitivity and glucose disposal, which are highest

in the morning and decline toward the evening. Liver-specific Bmal1

mutant studies have revealed liver clock promotion of gluconeogenesis,

glycogenolysis, and mitochondrial oxidative metabolism in the sleep/

fasting period while promoting glycogen synthesis in the wake/feeding

period. Muscle-specific Bmal1-deficient mice display reduced glucose

tolerance, concomitant with lower levels of proteins involved in glucose

uptake by muscle cells (e.g., the glucose transporter GLUT4). Ablation

of the Cry1 and Cry2 repressors in the negative limb of the clock alters

glucagon and glucocorticoid signaling in the liver, contributing to

hyperglycemia and impaired glucose tolerance in these mutant mice.

Together, these genetic studies in mice suggest a role for tissue-specific

clocks in the partitioning of energy utilization across the sleep-wake

cycle.

Importantly, peripheral clocks also interact with other environmental factors such as diet and time of feeding. For example, high-fat

feeding leads not only to obesity and metabolic syndrome in mice,

but also to perturbed clock gene expression across multiple peripheral

tissues and a disrupted sleep-wake/fasting-feeding cycle, as revealed

by increased activity and feeding during the daytime. Furthermore,

mice that are fed a high-fat diet exclusively during their (inactive) light

phase gain significantly more weight than mice that are fed the same

diet during the dark period—the active period for mice. Additionally,

the metabolic phenotypes arising from ad lib high-fat feeding can be

significantly ameliorated by restricting the time of high-fat feeding

exclusively to the dark period. Animals with disrupted clock throughout the hypothalamus and SCN exhibit mistimed eating and adverse

metabolic rhythms that can be restored by dark-only feeding. Timerestricted feeding can also increase the activity of brown adipose tissue

in mice and reduce hepatic glucose production to instead promote beta

oxidation of fatty acids. The potential clinical utility of time-restricted

feeding has been corroborated in human interventional studies. These

have demonstrated that dietary interventions modulate transcriptional

rhythms across tissues and that time-restricted feeding can improve

metabolic homeostasis as well as promote weight loss. Time-restricted

feeding may also modulate central regulation of sleep and hunger, as

studies have found that humans who restrict their food intake to a

3809The Role of Circadian Biology in Health and Disease CHAPTER 485

shorter than ad lib period also consume fewer daily calories and report

both lower hunger and improved sleep.

Finally, animal studies have further shown that when the light-dark

cycle is disrupted or animals are subjected to conditions mimicking

“jetlag”—by artificially advancing or delaying the daily light period—

there is desynchronization among circadian clocks and subsequent

weight gain. Accumulating evidence in humans also finds that circadian misalignment both disrupts and desynchronizes circadian clocks

across tissues. Clinical studies that have sampled tissues such as blood,

skeletal muscle, and adipose tissue at regular intervals have observed

day-night rhythms in clock genes and metabolic genes and disruption

of oscillating genes by sleep-wake interventions. Prolonged circadian

misalignment using forced desynchrony protocols reduces insulin

sensitivity in the pre- and post-prandial state. Under such conditions,

insulin secretion fails to suppress glucose levels, suggesting inadequate

β-cell compensation. Moreover, resting metabolic rate declines significantly both in the awake and sleeping state, altogether providing

potential explanations why shift work can increase the risk of obesity,

type 2 diabetes, and the metabolic syndrome.

Human genetic association studies also support a role for clock genes

in metabolic homeostasis and β-cell function. Carriers of a certain

BMAL1 polymorphism have a greater risk of developing type 2 diabetes,

while CLOCK variants have been found to interact with diet, such that

variants can have a protective effect on insulin sensitivity in individuals

with high monounsaturated fat intake or in individuals provided a lowfat diet. Instead, the minor allele of another CLOCK gene variant has

been associated with increased waist circumference, but only in those

with high saturated fat intake. Similarly, NPAS2 and BMAL1 variants

have been associated with a greater risk of hypertension. Melatonin

receptor MTNR1B gene variants that result in increased expression

of MTNR1B have been associated with elevated fasting blood glucose

levels and reduced insulin secretion irrespective of their level of glycemic control, consistent with the known effect of melatonin on insulin

secretion and lower insulin secretion in the evening. These association

studies highlight the role of the circadian system in metabolism, as

well as the potential for interactions of external perturbations—such as

circadian misalignment—with a protective or adverse genetic profile.

A large proportion of society recurrently shifts sleep-wake and eating

times between working/nonfree days and free days. This social jetlag

has been increasingly tied to metabolic disruptions, including a greater

risk of obesity and type 2 diabetes. As this involves recurrent phase

advances and phase delays—like shift work but of smaller magnitude—it

is possible that social jetlag, and often interlinked eating jetlag, also

results in perturbed rhythms of energy expenditure, in combination

with disruptions to the circadian hunger drive, further increasing the

risk of obesity. Repeated shifts in the food- and SCN-driven rhythm

of insulin release may similarly over time increase the risk of type 2

diabetes. Shifted feeding rhythms in relation to the sleep-wake cycle

and the timing of SCN activity may be causally involved in this pathogenesis. This is exemplified by the disorders known as night-eating

syndrome and sleep-related eating disorder. In the former syndrome,

a large part of daily calorie consumption occurs in the evening and

nighttime hours, and this shifted meal pattern has been associated with

a delayed timing of the internal clock. Some evidence exists that these

syndromes are associated with obesity. Individuals who report sleeping

fewer hours or who are subjected to restricted sleep for a few consecutive days have also been found to consume more calories, especially

later in the evening, a period during which prolonged fasting favors

oxidative fuel utilization. As such, this may explain why sleep restriction increases the risk of obesity. These associations have also been

observed in individuals with later onset of sleep, i.e., evening chronotypes. Night-eating syndrome and later chronotypes have also been

linked to type 2 diabetes and may be more common than other eating

disorders such as binge-eating disorder. Both conditions have also

been found to be associated with impaired glycemic control—such as a

greater likelihood of hemoglobin A1c values exceeding 7%—in patients

already suffering from type 2 diabetes. This emphasizes how proper

alignment of internal circadian rhythms with external factors are key

contributing factors for long-term metabolic homeostasis.

Circadian Clocks in Relation to Brain Health and Cognition

Molecular circadian clocks are present not only within the extra-SCN

regions of the brain but also in neurons, astrocytes, microglia, and cells

of the blood-brain barrier. Emphasizing the functional significance of

properly aligned clocks for brain health, shift workers have been found

to have decreased gray matter in brain regions involved in memory and

executive functions, with more notable effects in individuals who had

shorter recovery periods between the onset of each shift work cycle.

Adults performing rotating shift work for many years have also been

shown to exhibit signs of accelerated cognitive aging. Notably, evidence

suggests that these effects may be reversible, as those who have stopped

carrying out shift work exhibit normal cognitive performance 5 or

more years later.

Studies have also uncovered an important role for perturbed circadian and sleep-wake rhythms in neurodegenerative conditions such as

Alzheimer’s disease (AD), Huntington’s disease (HD), and Parkinson’s

disease (PD). Amyloid beta (Aβ), a key pathognomonic component

of AD, normally exhibits circadian fluctuations in the extracellular

space in the brain, as well as in the cerebrospinal fluid and plasma in

humans, peaking during the active period and falling during sleep. Of

note, these daily rhythms of Aβ accumulation are dampened in mice

that are prone to develop AD; reduced plasma Aβ fluctuations have

also been noted in older compared with younger individuals. Animal

studies indicate that removal of Aβ (and other neurotoxic substances)

during the nighttime sleep period is facilitated by a lymphatic-like

system that relies on glial cells (the “glymphatic” system). Relevance of

this system to humans is suggested by the observation that slow-wave

sleep is accompanied by hemodynamic fluctuations that alter the flow

of cerebrospinal fluid, which can remove toxins such as Aβ. Consistent

with a role for circadian rhythms in the pathogenesis of AD, ablation of

core clock genes throughout the brain, within subregions of the brain

or within glia, leads to pathology such as oxidative stress, neuronal cell

death, and scarring of brain tissue (astrogliosis). Furthermore, perturbed light-dark cycles increased pathology associated with oxidative

stress, and single nucleotide polymorphisms in Clock and Bmal1 have

been associated with increased risk of developing AD.

Evidence also indicates that the relationship between the circadian/

sleep-wake system and AD is bidirectional. For example, patients suffering from AD exhibit several signs of perturbed circadian rhythms,

the most prominent of such phenomena being “sundowning,” whereby

AD patients become more agitated and exhibit delirium-like symptoms

in the afternoon or evening. Studies have furthermore indicated that

in severe forms of AD, the circadian rhythm is phase delayed. Aged

AD-prone mice also display perturbed sleep-wake patterns, which can

be corrected by immunization against Aβ or by an orexin antagonist.

Further research will help uncover the primary pathogenic contribution of the circadian system, and its independent contribution from

perturbed sleep, in conditions like AD. Notably, evidence suggests

that interventions that increase daytime light exposure and include

melatonin supplementation are able to ameliorate symptoms of AD,

presumably by counteracting disrupted circadian rhythms.

While the relation between shift work and depression has not been

extensively studied, disruption of sleep and circadian rhythms and the

pathogenesis of depression are intimately interlocked. Clock genes

have also been implicated in depression and mood both in animal and

human studies. Polymorphisms of genes that regulate sleep and circadian rhythms—for instance, a long gene variant of PER3—have also

been linked to bipolar disorder and schizophrenia, while CRY2 and

CLOCK gene polymorphisms are associated with seasonal affective disorder, a type of depression arising in the fall and winter months when

the levels of sunlight are lowest. Bipolar disorder is furthermore often

triggered by circadian disruptions or curtailed sleep. Both bipolar disorder and schizophrenia have been linked to various forms of circadian

disruption following disease onset, and a critical component of disease

treatment often involves normalizing sleep and sleep-wake rhythms.

Sleep deprivation by itself is known to reduce alertness, impair

decision-making, and increase risk for accidents—after 18–24 h of continuous wakefulness, several skills exhibit the same degree of decline as

following mild alcohol intoxication. However, cognitive abilities may

3810 PART 20 Frontiers

suffer even further when sleep restriction is combined with circadian

misalignment as in shift work. In one study, participants were subjected

to ~43-h long days in parallel with reduced sleep (equivalent to 5.6 h of

sleep in a 24-h period), yielding a forced desynchrony protocol coupled

with sleep loss. When subjects were tested at the nadir of their circadian period, the subjects’ reaction speed dropped almost by an order

of magnitude compared with controls. In another study, researchers

noted almost a 36% greater incidence of serious medical errors in

resident interns who regularly worked 24-h or longer shifts compared

with those who were randomly assigned to work up to 16-h-long shifts.

Furthermore, errors that resulted in patient death were three times

more likely to occur in residents working extended hours compared

with those who only worked up to 16-h-long shifts.

Circadian Regulation of Gastrointestinal Homeostasis and

the Microbiota Physiologic aspects of the gastrointestinal (GI)

tract exhibit day-night variations that anticipate and prepare for food

intake and digestion during the active period. Gastric emptying and

colonic motility are considerably greater during the active phase, as

the phasic motor program supporting movement of digested material

along the intestine is approximately twice as fast during the day compared with night. Bile acid secretion also exhibits circadian rhythmicity

in the intestine, as does absorption and the expression of many nutrient

uptake transporters in the intestinal wall, including the main glucose

transporter protein SGLT1. The permeability of the intestinal wall also

varies throughout the sleep-wake cycle, and mice exposed to chronic

sleep fragmentation exhibit increased intestinal permeability, which

may enable inflammatory molecules from bacteria to reach the systemic circulation.

The composition and function of the microbe population residing

in the intestine (i.e., the gut microbiota) also display circadian rhythmicity, orchestrated by both host circadian clock gene expression and

food intake rhythms. Accordingly, circadian disruption, either by

environmental or genetic means, perturbs these microbial rhythms,

disrupting both bacterial levels and the metabolic functions of the gut

microbiota. For example, alterations in the expression and functions of

the gut microbiota have been noted in humans exposed to acute jetlag,

and evidence suggests that curtailing sleep, which often accompanies

shift work and jetlag, can alter the gut microbiota. By increasing local

and systemic inflammation, circadian disruption of the gut microbiota

may be causally involved in the increased risk of inflammatory bowel

disease (Crohn’s disease and ulcerative colitis) and colon cancer in shift

workers. Biological sex differences have also been reported, as female

mice display more pronounced microbial rhythms. Interestingly, the

gut microbiome has also been shown to influence the rhythms of host

tissues, such as the intestine and liver, that also appear sex-specific.

This indicates that a bidirectional relationship exists between tissues

that regulate metabolic processes and the gut microbiome across the

sleep-wake cycle. These findings may furthermore have clinical implications, given that the gut microbiome may both directly (in the gut

lumen) and indirectly (through host-microbiota interactions such as

through signaling molecules) impact metabolic responses and pharmacokinetic and pharmacodynamic properties of therapeutic drugs

across the 24-h day-night cycle.

Cardiovascular Health and the Circadian Clock An early

epidemiologic observation was a greater incidence of myocardial

infarction in the morning hours, with the lowest risk during the period

preceding sleep. Other cardiovascular outcomes such as sudden cardiac death and syncope also exhibit a daily peak in the morning. Blood

pressure (BP) typically peaks around 2100 h and decreases later during

sleep, partially due to a circadian nighttime dip of around 3–6 mmHg

in systolic BP and 2–3 mmHg in diastolic BP. A dip in BP of either

<10% or >20% during normal sleep has been associated with worse

cardiovascular prognosis and risk of dementia. Heart rate also typically

decreases during sleep, while mistimed sleep leads to higher heart rate

during sleep time. Studies also suggests that heart muscle may be more

tolerant to hypoxia and thus fare better under surgery scheduled for the

afternoon, due to timing of cellular programs driven by the cell autonomous clock in cardiomyocytes. Thus, a combination of factors—which

may also involve altered glucocorticoid levels and increased platelet

aggregation—may contribute to a greater risk of cardiovascular disease

in the morning. Subsequent epidemiologic studies also have demonstrated that shift work increases the risk of dyslipidemia and hypertension, as well as the risk of coronary heart disease, including myocardial

infarction. These findings are in line with interventional findings in

which circadian misalignment has been induced either by inverting the

sleep-wake cycle or by imposing 28-h days on healthy human subjects.

These studies have found that circadian misalignment elevates 24-h

BP, particularly during sleep. These changes may be causally related to

how the autonomic system is regulated during sleep, as evidenced by

reduced vagal cardiac control when the sleep-wake cycle is inverted.

Circadian Disruption and Cancer In 2007, the International

Agency for Research on Cancer declared that shift work that involves

circadian disruption is likely carcinogenic to humans. While evidence

for an association between shift work and general cancer incidence

is mixed, accruing evidence supports a link between shift work and

increased risk of developing colon and breast cancer, as well as having a

poorer cancer prognosis. Telomere shortening, a phenomenon in aging

that destabilizes the genome, has also been observed in shift workers

as well as in individuals suffering from short sleep. Such changes may

reduce the ability of damaged or senescent cells to undergo apoptosis

and, instead, lead to uninhibited cell growth and cancer. An indirect

role for the circadian clock has also come from retrospective studies on

how cancer risk is related to food timing and duration of the nighttime

fast in humans. In combination with interventional time-restricted

studies, these indicate that by portioning food intake to a restricted

period of the day, optimized portioning of circadian processes confers a reduced risk of potentially carcinogenic cell damage. Studies of

recurring fasting have also shown that it lowers the risk and delays the

onset of cancer.

Experimental genetic evidence has also implicated clock disruption

as a factor in tumorigenesis. Genetic loss of Per2 or Bmal1 has been

shown to promote lung tumorigenesis, while studies in Per2 mutant

mice have also revealed increased radiation-induced lymphoma associated with dysregulation of the cell cycle. However, disruption of the

Cry gene in mice has also been implicated in tumor protection due

to increased susceptibility to cell death. In contrast, pharmacologic

overactivation of REV-ERB may impair growth of glioblastomas. While

epidemiologic, experimental, and chronotherapeutic evidence (see section “Chronotherapy and Future Directions”) suggests a link between

circadian disruption and cancer, the precise role of circadian systems

in tumorigenesis remains to be determined.

Circadian Regulation of the Immune System Circadian misalignment and sleep restriction both alter population levels of immune

cells and decrease the ability of immune cells to produce reactive radicals, in part likely through disruption of cytokine rhythms. Chronic

circadian disruption may thereby impair the immune system’s ability

to conduct immunosurveillance at the proper time of day. This may

reduce the ability to mount an appropriate pathogen-induced effector

(cytotoxic T-cell) response during the active period, as well as impair

the more long-term adaptive immune response, which is favored by the

cytokine milieu (such as surges in prolactin and GH) that accompanies

the recovery/sleep phase. Instead, circadian misalignment increases a

range of clinically-used inflammatory markers (e.g., C-reactive protein, tumor necrosis factor α, and interleukin 6), and such changes

have been noted even when the sleep-wake cycle is only prolonged to

a slightly longer than normal 24.6-h day. While similar effects are also

observed following acute total sleep deprivation or recurrent partial

sleep restriction, circadian misalignment has been found to promote

an even more pronounced elevation of such markers. Genetic clock

disruption in peritoneal macrophages has also revealed clock control

of Toll-like receptor 9, which is responsible for identifying molecules

from foreign pathogens. Clock knockout mice also have reduced T-cell

antigen response, and mice immunized during the day had a stronger

T-cell response than mice immunized at night, supporting regulation

of the immune system by the clock. Similar mechanisms likely take

place in humans, as clinical studies have noted an impaired vaccine

3811The Role of Circadian Biology in Health and Disease CHAPTER 485

response following sleep disruption, and several studies have noted an

improved immunogenic response to various antigens when vaccinated

in the morning compared with afternoon.

Aging and the Circadian Clock Instability in the clock system

is an often-overlooked hallmark of aging. Aging is associated with a

decline in the robustness of intrinsic rhythmic processes at the behavioral, physiologic, and molecular levels in both human and animal

models. At the behavioral level, aging leads to reduced and fragmented

sleep, dampened locomotor activity and feeding rhythms, and a

reduced ability to entrain to light, as old rodents are 20 times less sensitive to the entraining effects of light relative to young animals. Even

middle-aged individuals exposed to jetlag exhibit more symptoms of

circadian misalignment, such as more time awake and reduced alertness, compared with young individuals. On a physiologic level, some

of the hallmarks of aging are a reduction in amplitude (e.g., flattening

of circadian pattern) that also impacts the signal during the evening

period (the wake maintenance zone). Aging also results in a phase

advance (e.g., a shift in the timing of the peak or nadir) in rhythms

of the endocrine and neuroendocrine systems, including sleep onset

and offset. For example, cortisol, dehydroepiandrosterone (DHEA),

and melatonin all have dampened rhythms and are phase advanced in

aging; the combination of such changes may, for instance, contribute

to more fragmented sleep and lower levels of restorative slow-wave

sleep in aged individuals. Relatedly, aging results in reduced peptide

expression in the SCN (VIP and AVP), cell loss in sleep-wake regions

(including the SCN), and reduced amplitude of rhythms of SCN electrical activity. Further, while the SCN-dependent body temperature

rhythm—a generally accepted marker for the integrity of circadian

rhythms—peaks in the evening and is lowest in the early morning in

young individuals, aged healthy subjects display a phase advance and

decrease in circadian amplitude in body temperature rhythms. Indeed,

evidence suggests that internal desynchrony between core body

temperature rhythms and the sleep-wake cycle may contribute to

age-associated circadian alterations.

On a molecular level, aging is associated with decreased expression

and altered diurnal profiles of several of the core clock genes, including Clock and Bmal1, within both SCN and peripheral tissues such as

heart and liver. The acute induction of Per1 in response to light was

markedly reduced in the SCN of aged mice compared with young mice,

potentially contributing to their delayed response to light entrainment.

Mice lacking Bmal1 die prematurely compared with control mice, consistent with premature accumulation of reactive oxygen species. These

mice have an accelerated onset of numerous age-related pathologies,

including cataracts, sarcopenia, reduced organ size, and decreased hair

growth. Instead, deficiency of cryptochrome, a repressor of the internal

clock repressor, has been associated with alterations in liver regeneration, while BMAL1 and PER2 may be important for proper neurogenesis in the hippocampus, a brain region in which adult mammals

normally exhibit continuous cell division. Altogether, this suggests that

the highly conserved circadian clock is important for regulating a wide

range of homeostatic processes, including cell-cycle pathways, which

when properly phased to each other promote organismal fitness.

Measurements of altered circadian rhythms with age may serve as a

useful biomarker for aging. An intriguing question is whether the decline

in amplitude of rhythms correlates with a decline in function and, importantly, whether restoration of these rhythms with age, through either

behavioral or pharmacologic intervention, would delay the aging process.

Studies in mice indicate that behavioral and pharmacologic interventions

(including exercise) can restore circadian oscillations in aging. Similarly,

transplantation of the SCN from a young rat into an old rat “rescued” the

rhythms of both locomotor activity and corticotropin hormone (CRH),

suggesting that the SCN is an important target for age-related changes in

clocks. Physical activity or targeted therapeutics may therefore ameliorate

some of the circadian deterioration in aged humans.

■ CHRONOTHERAPY AND FUTURE DIRECTIONS

Chronopharmacology, the study of how the timing of drug administration may impact its effectiveness, is a rapidly emerging field. Since

physiologic processes vary across the day, the timing of administration

of medication may help optimize patient care. For example, since

endogenous cholesterol synthesis is rhythmic in liver and peaks during the early morning hours, administration of statins (HMG-CoA

reductase inhibitors) in the evening prior to bedtime has proven to be

more effective than daytime administration at reducing low-density

lipoprotein cholesterol (LDL-C) levels because the highest concentration of medication coincides with the peak in rhythmic endogenous

cholesterol production. Given that BP exhibits a 24-h rhythm—being

lowest during sleep—angiotensin-converting enzyme (ACE) inhibitors

have been shown to be most effective at night to normalize the BP

rhythms, restoring the nighttime dip in BP that is foremost tied to the

occurrence of sleep. Numerous studies have also demonstrated that

administration of cancer treatments at specific times of the day can

increase chemotherapy effectiveness while also decreasing toxicity

for a wide range of drugs. For example, 5-fluorouracil works best to

treat colorectal cancer when administered at night, a time when the

cancerous cells are more vulnerable while normal cells are quiescent

and therefore less sensitive. Doxorubicin administration early in the

morning to treat ovarian cancer has also been shown to be less toxic, as

white blood cells recover faster than if the drug is given in the evening.

Finally, the more severe morning symptoms of rheumatoid arthritis

are linked to increased inflammation toward the evening; therefore,

prevention of the nighttime upregulation of the immune/inflammatory

reaction is more effective when glucocorticoids are administered with a

nighttime release formulation.

Recognition of circadian rhythms is also critical for diagnoses and

treatment of endocrine disorders. The diagnosis of Cushing’s syndrome, which is characterized by hypercortisolemia, might be missed if

the patient’s cortisol levels are measured in the morning, when endogenous cortisol production peaks. Therefore, clinical diagnosis requires

cortisol to be measured in the late evening when the levels of this

hormone should typically be low. On the other hand, adrenal insufficiency is diagnosed by measuring cortisol in the morning when at its

physiologic peak, and glucocorticoid therapy for these patients aims to

mimic the endogenous rhythms of cortisol, as short-acting synthetic

glucocorticoids are usually given several times a day in tapering doses,

such that the largest amount is taken in the morning and the smallest

in the evening. Diabetes is another endocrine disorder intimately tied

to circadian rhythms. Oral glucose tolerance, which is commonly used

to diagnose diabetes, is worse in the afternoon and evening compared

with the morning. This likely stems from greater daytime insulin sensitivity within peripheral tissues and reduced insulin secretion during

the night. Similarly, due to a surge in hormone levels in the morning,

diabetes patients may suffer from the dawn phenomenon (or dawn

effect), an abnormally high morning increase in blood glucose due to

impaired response in insulin secretion. A related phenomenon that can

be tied to evening timing of insulin doses is the “rebound” or Somogyi

effect. In this scenario, the initially noted clinical sign in the form of

elevated glucose levels may be noted in the morning. However, the

underlying cause is hypoglycemia occurring during the night, which

produces a counterregulatory hormonal response that subsequently

results in morning hyperglycemia. As patients with type 2 diabetes

often have grossly impaired daily cycles of insulin secretion and glucose tolerance, this further highlights that time of day is an important

consideration for the diagnosis and treatment of metabolic disorders

such as type 2 diabetes.

As our knowledge of the complexity of how circadian processes

modulate physiology deepens, further advances to rationally develop

new strategies for treatments of disorders affected by circadian misalignment are essential. For example, novel compounds have begun

to emerge from unbiased drug discovery screens that in cell- and

animal-based assays impact circadian clock components, either shortening or lengthening the period. These compounds include CRY

stabilizers and various inhibitors of CKIδ, CKIε, and GSK-3. Pharmacologic control of the circadian cycle may be useful in the treatment

of circadian disorders and metabolic disturbances with a circadian

component. Understanding how the circadian clock controls biological functions will shed new light onto the pathogenesis of metabolic

3812 PART 20 Frontiers

disorders with a circadian component, such as type 2 diabetes and

metabolic syndrome, and will yield insight into how timing of drug

delivery will impact patient care.

Acknowledgment

The authors would like to thank Billie Marcheva for her help with the

figures and tables.

■ FURTHER READING

Allada R, Bass J: Circadian mechanisms in medicine. N Engl J Med

384:550, 2021.

Buxton OM et al: Adverse metabolic consequences in humans of

prolonged sleep restriction combined with circadian disruption. Sci

Transl Med 4:129ra43, 2012.

Cedernaes J: et al: Transcriptional basis for rhythmic control of hunger and metabolism within the AgRP neuron. Cell Metab 29:1078,

2019.

Dibner C et al: The mammalian circadian timing system: Organization and coordination of central and peripheral clocks. Annu Rev

Physiol 72:517, 2010.

Hatori M et al: Time-restricted feeding without reducing caloric

intake prevents metabolic diseases in mice fed a high-fat diet. Cell

Metab 15:848, 2012.

Kervezee L et al: Metabolic and cardiovascular consequences of shift

work: The role of circadian disruption and sleep disturbances. Eur J

Neurosci 51:396, 2020.

Scheer FA et al: Adverse metabolic and cardiovascular consequences

of circadian misalignment. Proc Natl Acad Sci USA 106:4453, 2009.

Takahashi JS: Transcriptional architecture of the mammalian circadian clock. Nat Rev Genet 18:164, 2016.

Turek FW et al: Obesity and metabolic syndrome in circadian clock

mutant mice. Science 308:1043, 2005.

The field of human biology has progressed over the past three centuries

largely as a result of the reductionist approach to the scientific problems that challenge the discipline. Biologists study the experimental

response of a variable of interest in a cell or organism while holding

all other variables constant. In this way, it is possible to dissect the

individual components of a biologic system and assume that a thorough understanding of a specific component (e.g., an enzyme or a

transcription factor) will provide sufficient insight to explain the global

behavior of that system (e.g., a metabolic pathway or a gene network,

respectively). Biologic systems are, however, much more complex than

this approach assumes and manifest behaviors that frequently (if not

invariably) cannot be predicted from knowledge of their component

parts characterized in isolation. Growing recognition of this shortcoming of conventional biologic research has led to the development of a

new discipline, systems biology, which is defined as the holistic study

of living organisms or their cellular or molecular network components

to predict their response to perturbations. Concepts of systems biology can be applied readily to human disease and therapy and define

the field of systems pathobiology, in which genetic or environmental

perturbations produce disease and drug perturbations restore normal

system behavior.

Systems biology evolved from the field of systems engineering in

which a linked collection of component parts constitutes a network

486 Network Medicine:

Systems Biology in

Health and Disease

Joseph Loscalzo

whose output the engineer wishes to predict. The simple example of

an electronic circuit can be used to illustrate some basic systems engineering concepts. All the individual elements of the circuit—resistors,

capacitors, transistors—have well-defined properties that can be characterized precisely. However, they can be linked (wired or configured)

in a variety of ways, each of which yields a circuit whose response to a

voltage applied across it is different from the response of every other

configuration. To predict the circuit’s (i.e., system’s) behavior, the

engineer must study its response to perturbation (e.g., voltage applied

across it) holistically rather than its individual components’ responses

to that perturbation. Viewed another way, the resulting behavior of the

system is greater than (or different from) the simple sum of its parts,

and systems engineering utilizes rigorous mathematical approaches

to predict these complex, often nonlinear, responses. By analogy to

biologic systems, one can reason that detailed knowledge of a single

enzyme in a metabolic pathway or of a single transcription factor in a

gene network will not provide sufficient detail in context to predict the

output of that metabolic pathway or transcriptional network, respectively. Only a systems-based approach will suffice.

It has taken biologists a long time to appreciate the importance of

systems approaches to biomedical problems. Reductionism has reigned

supreme for many decades, largely because it is experimentally and

analytically simpler than holism and because it has provided insights

into biologic mechanisms and disease pathogenesis that have led to

successful therapies. However, reductionism cannot solve all biomedical problems. For example, the so-called off-target effects of new

drugs that frequently limit their adoption likely reflect the failure of a

drug to be studied in holistic context, that is, the failure to explore all

possible actions aside from the principal target action for which it was

developed. Other approaches to understanding biology are, therefore,

clearly needed. With the growing body of genomic, proteomic, and

metabolomic data sets in which dynamic changes in the expression of

many genes and many metabolites are recorded after a perturbation

and with the growth of rigorous mathematical approaches to analyzing

those changes, the stage has been set for applying systems engineering

principles to modern biology.

Physiologists historically have had more of a (bio)engineering perspective on the conduct of their studies and have been among the first

systems biologists. Yet, with few exceptions, they, too, have focused

on comparatively simple physiologic systems that are tractable using

conventional reductionist approaches. Efforts at integrative modeling

of human physiologic systems, as first attempted by Guyton for blood

pressure regulation, represent one application of systems engineering

to human biology. These dynamic physiologic models often focus on

the acute response of a measurable physiologic parameter to a system

perturbation, and do so from a classic analytic perspective in which all

the conventional physiologic determinants of the output parameter are

known and can be modeled quantitatively.

Until recently, molecular systems analysis has been limited owing to

inadequate knowledge of the molecular determinants of a biologic system of interest. Although biochemists have approached metabolic pathways from a systems perspective for >50 years, their efforts have been

limited by the inadequacy of key information for each enzyme (Km,

kcat, and concentration) and substrate (concentration) in the pathway.

With increasingly rich molecular data sets available for systems-based

analyses, including genomic, transcriptomic, proteomic, and metabolomic data, molecular biologists and biochemists are now poised to

use systems biology approaches to explore biologic and pathobiologic

phenomena.

PROPERTIES OF COMPLEX

BIOLOGIC SYSTEMS

To understand how best to apply the principles of systems biology

to human biomedicine, it is necessary to review briefly the building

blocks of any biologic system and the determinants of system complexity. All systems can be analyzed by defining their static topology

(architecture) and their dynamic (i.e., time-dependent) response to

perturbation. In the discussion that follows, system properties are

described that derive from the consequences of topology (form) or

3813 Network Medicine: Systems Biology in Health and Disease CHAPTER 486

Random network Scale-free network

P(k) = e–k

k

Poisson distribution

log k

Power law distribution

<k>

Few nodes

highly linked

Many nodes

sparsely linked

k = degree or #

nodal connections

P (k)

P(k) = k–γ

log P(k)

m = γ

FIGURE 486-1 Network representations and their distributions. A random network is depicted on the left, and its Poisson distribution of the number of nodal connections

(k) is shown in the graph below it. A scale-free network is depicted on the right, and its power law distribution of the number of nodal connections (k) is shown in the graph

below it. Highly connected nodes (hubs) are lightly shaded.

dynamic response (function). Any system of interacting elements can

be represented schematically as a network in which the individual

elements are depicted as nodes and their connections are depicted

as links. The nature of the links among nodes reflects the degree of

complexity of the system. Simple systems are those in which the nodes

are linearly linked with occasional feedback or feedforward loops

modulating system throughput in highly predictable ways. By contrast,

complex systems are nodes that are linked in more complicated, nonlinear networks; the behavior of these systems by definition is inherently

more difficult to predict owing to the nature of the interacting links,

the dependence of the system’s behavior on its initial conditions, and

the inability to measure the overall state of the system at any specific

time with great precision. Complex systems can be depicted as a network of lower-complexity interacting components or modules, each of

which can be reduced further to simpler analyzable canonical motifs

(such as feedback and feedforward loops or negative and positive

autoregulation); however, a central property of complex systems is

that simplifying their structures by identifying and characterizing the

individual nodes and links or even simpler substructures does not necessarily yield a predictable understanding of a system’s behavior. Thus,

the functioning system is greater than (or different from) the sum of its

individual, tractable parts.

Defined in this way, most biologic systems are complex systems

that can be represented as networks whose behaviors are not readily

predictable from simple reductionist principles. The nodes, for example, can be metabolites that are linked by the enzymes that cause their

transformations, transcription factors that are linked by the genes

whose expression they influence, or proteins in an interaction network

that are linked by cofactors that facilitate interactions or by thermodynamic forces that facilitate their physical association. Biologic systems

typically are organized as scale-free, rather than stochastic, networks of

nodes. Scale-free networks are those in which a few nodes have many

links to other nodes (highly linked nodes, or hubs), but most nodes

have only a few links (weakly linked nodes). The term scale-free refers

to the fact that the connectivity of nodes in the network is invariant

with respect to the size of the network. This is quite different from

two other common network architectures: random (Poisson) and

exponential distributions. Scale-free networks can be mathematically

described by a power law that defines the probability of the number of

links per node, P(k) = k−γ, where k is the number of links per node and

γ is the slope of the log P(k) versus log(k) plot; this unique property of

most biologic networks is a reflection of their self-similarity or fractal

nature (Fig. 486-1).

There are unique properties of scale-free biologic systems that

reflect their evolution and promote their adaptability and survival. Biologic networks likely evolved one node at a time in a process in which

new nodes are more likely to link to a highly connected node than

to a sparsely connected node. Furthermore, scale-free networks can

become sparsely linked to one another, yielding more complex, modular scale-free topologies. This evolutionary growth of biologic networks

has three important properties that affect system function and survival.

First, this scale-free addition of new nodes promotes system redundancy, which minimizes the consequences of errors and accommodates

adverse perturbations to the system robustly with minimal effects on

critical functions (unless the highly connected nodes are the focus of

the perturbation). Second, this resulting network redundancy provides

a survival advantage to the system. In complex gene networks, for

example, mutations or polymorphisms in weakly linked genes account

for biodiversity and biologic variability without disrupting the critical

functions of the system; only mutations in highly linked (essential)

genes (hubs) can shut down the system and cause embryonic lethality.

Third, scale-free biologic systems facilitate the flow of information

(e.g., metabolite flux) across the system compared with randomly

organized biologic systems; this so-called “small-world” property of the

system (in which the clustered nature of the highly linked hubs defines

a local neighborhood within the network that communicates through

weaker, less frequent links to other clusters) minimizes the energy cost

for the dynamic action of the system (e.g., minimizes the transition

time between states in a metabolic network).

These basic organizing principles of complex biologic systems

lead to three unique properties that require emphasis. First, biologic

systems are robust, which means that they are quite stable in response

to most changes in external conditions or internal modification. Second, a corollary to the property of robustness is that complex biologic

3814 PART 20 Frontiers

systems are sloppy, which means that they are insensitive to changes

in external conditions or internal modification except under certain

uncommon conditions (i.e., when a hub is involved in the change).

Third, complex biologic systems exhibit emergent properties, which

means that they manifest behaviors that cannot be predicted from the

reductionist principles used to characterize their component parts.

Examples of emergent behavior in biologic systems include spontaneous, self-sustained oscillations in glycolysis; spiral and scroll waves of

depolarization in cardiac tissue that cause reentrant arrhythmias; and

self-organizing patterns in biochemical systems governed by diffusion

and chemical reaction.

APPLICATIONS OF SYSTEMS BIOLOGY

TO PATHOBIOLOGY

The principles of systems biology have been applied to complex pathologic processes with growing successes. The key to these applications is

the identification of emergent properties of the system under study in

order to define novel, otherwise unpredictable (i.e., from the reductionist perspective) methods for regulating the system’s response. Systems

biology approaches have been used to characterize epidemics and ways

to control them, taking advantage of the scale-free properties of the network of infected individuals that constitute the epidemic. Through the

use of a systems analysis of a neural protein-protein interaction network,

unique disease-modifying proteins have been identified that are common to a wide range of cerebellar neurodegenerative disorders causing

inherited ataxias. Systems analysis and disease network construction of

a pulmonary arterial hypertension network led to the identification of

a unique disease module involving a pathway governed by microRNA

21 and, more recently, to a novel profibrotic pathway that promotes

vascular disease progression. Systems biology models have been used

to dissect the dynamics of the inflammatory response using oscillatory

changes in the transcription factor nuclear factor (NF)-κB as the system

output. Systems biology principles also have been used to predict the

development of an idiotypy–anti-idiotypy antibody network, describe

the dynamics of species growth in microbial biofilms, and analyze the

innate immune response. In each of these examples, a systems (patho)

biology approach provided insights into the behavior of these complex

systems that could not have been recognized with conventional scientific

reductionism.

A unique application of systems biology to biomedicine is in the

area of drug development. Conventional drug development involves

identifying a potential target protein and then designing or screening

compounds to identify those that inhibit the function of that target.

This reductionist analysis has identified many potential drug targets

and drugs, yet only when a drug is tested in animal models or humans

are the systems consequences of the drug’s action revealed; not

uncommonly, so-called off-target effects may become apparent and be

sufficiently adverse for researchers to cease development of the agent.

A good example of this problem is the unexpected outcomes of the

vitamin B–based regimens for lowering homocysteine levels. In these

trials, plasma homocysteine levels were reduced effectively; however,

there was no effect of this reduction on clinical vascular endpoints.

One explanation for this outcome is that one of the B vitamins in

the regimen, folate, has a panoply of effects on cell proliferation and

metabolism that probably offset its homocysteine-lowering benefits,

promoting progressive atherosclerotic plaque growth and its consequences for clinical events. In addition to these types of unexpected

outcomes exerted through pathways that were not considered ab initio,

conventional approaches to drug development typically do not take

into consideration the possibility of emergent behaviors of the organism or the metabolic pathway or the transcriptional network of interest

that may be influenced by the drug. Thus, a systems-based analysis of

potential drugs (drug-target network analysis) can benefit the development paradigm both by enhancing the likelihood that a compound

of interest will not manifest unforeseen adverse effects and by promoting novel analytic methods for identifying unique control points

or pathways in metabolic or genetic networks that would benefit from

drug-based modulation, including drug combinations. In addition and

importantly, unbiased knowledge about the systems behavior of a drug

can lead to rational approaches to drug repurposing.

SYSTEMS PATHOBIOLOGY AND

HUMAN DISEASE CLASSIFICATION:

NETWORK MEDICINE

Perhaps most important, systems pathobiology can be used to revise

and refine the definition of human disease. The classification of

human disease used in this and all medical textbooks derives from the

correlation between pathologic analysis and clinical syndromes that

began in the nineteenth century. Although this approach has been very

successful, serving as the basis for the development of many effective

therapies, it has major shortcomings. Those shortcomings include a

lack of sensitivity in defining preclinical disease, a primary focus on

overtly manifest disease, failure to recognize different and potentially

differentiable causes of common late-stage pathophenotypes, and

a limited ability to incorporate the growing body of molecular and

genetic determinants of pathophenotype into the conventional classification scheme.

Two examples will illustrate the weakness of simple correlation

analyses grounded in the reductionist principle of simplification

(Occam’s razor) used to define human disease. Sickle cell anemia, the

“classic” Mendelian disorder, is caused by a Val6Gln substitution in

the β chain of hemoglobin. If conventional genetic teaching holds,

this single mutation should lead to a single phenotype in patients

who harbor it (genotype-phenotype correlation). This assumption is,

however, false, as patients with sickle cell disease manifest a variety of

pathophenotypes, including hemolytic anemia, stroke, acute chest syndrome, bony infarction, and painful crisis, as well as an overtly normal

phenotype. The reasons for these different phenotypic presentations

include the presence of disease-modifying genes or gene products (e.g.,

hemoglobin F, hemoglobin C, glucose-6-phosphate dehydrogenase),

(stochastic) exposure to adverse environmental factors (e.g., hypoxia,

dehydration), and the genetic and environmental determinants of

common intermediate pathophenotypes or endophenotypes (i.e., variations in those generic pathologic mechanisms underlying all human

disease—inflammation, thrombosis/hemorrhage, fibrosis, cell proliferation, apoptosis/necrosis, immune response).

A second example of note is familial pulmonary arterial hypertension. This disorder is associated with well over 100 different mutations in three members of the transforming growth factor β (TGF-β)

superfamily alone—bone morphogenetic protein receptor-2 (BMPR-2),

activin receptor-like kinase-1 (Alk-1), and endoglin—as well as nine

other genes with high levels of evidence, and five genes with lower

levels of evidence. All of these different genotypes are associated with

a common pathophenotype, and each leads to that pathophenotype by

molecular mechanisms that range from haploinsufficiency to dominant negative effects. As only approximately one-fourth of individuals

in families that harbor these mutations manifest the pathophenotype,

other disease-modifying genes (e.g., the serotonin receptor 5-HT2B,

the serotonin transporter 5-HTT), genomic and environmental determinants of common intermediate pathophenotypes, and environmental exposures (e.g., hypoxia, infective agents [HIV], anorexigens)

probably account for the incomplete penetrance of the disorder.

On the basis of these and many other related examples, one can

approach human disease from a systems pathobiology perspective in

which each “disease” can be depicted as a network that includes the

following modules: the primary disease-determining elements of the

genome (or proteome, if posttranslationally modified), the diseasemodifying elements of the genome or proteome, environmental determinants, and genomic and environmental determinants of the generic

intermediate pathophenotypes. Figure 486-2 graphically depicts these

genotype-phenotype relationships as modules for the six common

disease types with specific examples for each type. Figure 486-3 shows

a network-based depiction of sickle cell disease using this type of modular approach.

Goh and colleagues developed the concept of a human disease

network (Fig. 486-4) in which they used a systems approach to

3815 Network Medicine: Systems Biology in Health and Disease CHAPTER 486

Dn

En

Gn In

D

E

G I P

D

G1

G2

Gn

E

I P

D1

G1

G2

Gn

E1

I1 P1

Dn

G1

G2

Gn

En

In Pn

D

E

I P

D1

E1

G1 I1 P

D1

E1

G I1 P1

Dn

En

G In Pn

Classic mendelian disorder:

Single phenotype

Classic mendelian disorder:

Multiple phenotypes

Example: Sickle cell disease

Classic mendelian disorder:

Multiple mutations, single phenotype

Example: Hypertrophic cardiomyopathy

Polygenic disorder:

Single phenotype

Example: Essential hypertension

Polygenic disorder:

Multiple phenotypes

Example: Ischemic heart disease

Environmental disorder

Example: Subacute bacterial endocarditis

FIGURE 486-2 Examples of modular representations of human disease. D, secondary human disease genome or proteome; E, environmental determinants; G, primary

human disease genome or proteome; I, intermediate phenotype; P, pathophenotype. (Reproduced with permission from J Loscalzo et al: Human disease classification in the

postgenomic era: A complex systems approach to human pathobiology. Molec Syst Biol 3:124, 2007.)

characterize the disease-gene associations listed in the Online Mendelian Inheritance in Man database. Their analysis showed that genes

linked to similar disorders are more likely to have products that physically associate and greater similarity between their transcription profiles than do genes not associated with similar disorders. In addition,

proteins associated with the same pathophenotype are significantly

more likely to interact with one another than with other proteins not

associated with the pathophenotype. Finally, these authors showed that

the great majority of disease-associated genes are not highly connected

genes (i.e., not hubs) and are typically weakly linked nodes within the

functional periphery of the network in which they operate. This set of

observations led to a rigorous analysis by Menche and colleagues of

299 diseases whose associated proteins were found to cluster in discrete

subnetworks or disease molecules within the global protein interaction

network.

This type of analysis validates the potential importance of defining disease on the basis of its systems pathobiologic determinants.

Clearly, doing so will require a more careful dissection of the molecular elements in the relevant pathways (i.e., more precise molecular

pathophenotyping), less reliance on overt (common) manifestations of

disease for their classification, and an understanding of the dynamics

(not just the static architecture) of the pathobiologic networks that

underlie pathophenotypes defined in this way. Figure 486-5 illustrates

the elements of a molecular network within which a disease module is

contained. This network is first identified by determining the interactions (physical or regulatory) among the proteins or genes that comprise it (the “interactome”). These interactions then define a topologic

module within which exist functional modules (pathways) and disease

modules. One approach to constructing this module is illustrated in

Fig. 486-6. Examples of the use of this approach in defining novel

determinants of disease are given in Table 486-1.

Principles of network and systems biology can also be applied to

clinical phenotyping. Rather than continuing to approach disease from

an inclusive perspective, precision systems analysis of the molecular

basis for disease must be linked to nuanced, deep phenotyping. This

strategy will lead to the identification of subgroups of patients with a

disease, each of which can be studied for differences in their disease

modules that account for their phenotypic differences.

As yet another potential consideration, one can argue that disease

reflects the later-stage consequences of the predilection of an organ

system to manifest a particular intermediate pathophenotype in

response to injury. This paradigm reflects a reverse causality view in

which a disease is defined as a tendency to heightened inflammation,

thrombosis, or fibrosis after an injurious perturbation. Where the

process is manifest (i.e., the organ in which it occurs) is less important

than that it occurs (with the exception of the organ-specific pathophysiologic consequences that may require acute attention). For example,

from this perspective, acute myocardial infarction (AMI) and its

consequences are a reflection of thrombosis (in the coronary artery),

inflammation (in the acutely injured myocardium), and fibrosis (at the

site or sites of cardiomyocyte death). In effect, the major therapies for

AMI address these intermediate pathophenotypes (e.g., antithrombotics, statins) rather than any organ-specific disease-determining

process. This paradigm would argue for a systems-based analysis that

3816 PART 20 Frontiers

G5,...,Gn

G1

G3

G2

G4

D5,...,Dn

D1

D3

D2

D4

I5 ... In

I1

I3

I2

I4

E5 ... En

E1

E3

E2

E4

PS5 ... PSn

PS1

PS3

PS2

PS4

P5 ... Pn

P1

P3

P2

P4

Primary disease

genome

Environmental

determinants

Pathophysiologic

states

Secondary disease

genome

Intermediate

pathophenotype

Pathophenome

A

HbS

TGF-β

HbC

HbF β-Thal

Immune

response

Inflammation

Hemolytic

anemia

Aplastic

anemia

Stroke

Painful

crisis

Disease-modifying

genes

Intermediate

phenotypes

Pathophenotype

G6PD

Apoptosis/

necrosis

Environmental

determinants

Hypoxia

Dehydration

Acute

chest

syndrome

Bone

infarct

Thrombosis

Infective

agent

B

FIGURE 486-3 A. Theoretical human disease network illustrating the relationships among genetic and environmental determinants of the pathophenotypes. Key: D,

secondary disease genome or proteome; E, environmental determinants; G, primary disease genome or proteome; I, intermediate phenotype; PS, pathophysiologic states

leading to P, pathophenotype. B. Example of this theoretical construct applied to sickle cell disease. Key: Red, primary molecular abnormality; gray, disease-modifying

genes; yellow, intermediate phenotypes; green, environmental determinants; blue, pathophenotypes. (Reproduced with permission from J Loscalzo et al: Human disease

classification in the postgenomic era: A complex systems approach to human pathobiology. Molec Syst Biol 3:124, 2007.)

would first identify the intermediate pathophenotypes to which a

person is predisposed, then determine how and when to intervene to

attenuate that adverse predisposition, and finally limit the likelihood

that a major organ-specific event will occur. Evidence for the validity

of this approach is found in the work of Rzhetsky and colleagues, who

reviewed 1.5 million patient records and 161 diseases and found that

these disease phenotypes form a network of strong pairwise correlations. This result is consistent with the notion that underlying genetic

predispositions to intermediate pathophenotypes form the predicate

basis for conventionally defined end-organ diseases.

Regardless of the specific nature of the systems pathobiologic

approach used, these analyses will lead to a drastic revision of the way

human disease is defined and treated, establishing the discipline of

network medicine, which reflects a fusion of the fields of systems biology

and network science in the study of disease. This will be a lengthy and

complicated process but ultimately will lead to better disease prevention and therapy and likely do so from an increasingly personalized,

molecularly precise perspective. The analysis of pathobiology from a

systems-based perspective is likely to help define specific subsets of

patients more likely to respond to particular interventions based on

3817 Network Medicine: Systems Biology in Health and Disease CHAPTER 486

A

Node size

41

34

30

25

21

15

10

5

Leukemia 1

Obesity

Diabetes

mellitus

Asthma

Hirschsprung’s disease

Hemolytic anemia

Blood groupSpherocytosis

Colon

cancer

Lymphoma

Deafness Retinitis

pigmentosa

Gastric cancer

Ataxia- telangiectasia

Prostate cancer

Fanconi

anemia

Mental retardation

Charcot-Marie-Tooth disease

Epilepsy

Parkinson’s disease

Alzheimer’s disease

Pseudohypo- Atherosclerosis aldosteronism

Hypertension

Leigh syndrome

Cardiomyopathy

Cataract

Myopathy

Muscular

dystrophy

Epidermolysis bullosa

Stroke

Myocardial infarction

Spinocerebellar alaxia

Complement component deficiency

Thyroid carcinoma

Breast cancer

B

KIT MEN1

KRAS TPS3

APC

FGFR3

FGFR2

MSH2

ERBB2

NF1

ACE

SCN4A

COL2A1 Bone

Cancer

Cardiovascular

Connective tissue

Dermatologic

Developmental

Ear, nose, throat

Endocrine

Gastrointestinal

Hematologic

Immunologic

Metabolic

Muscular

Neurologic

Nutritional

Ophthamologic

Psychiatric

Renal

Respiratory

Skeletal

Multiple

Unclassified

GJB2

LRPS

FBN1

PAX6

GNAS

ARX

PTEN

FIGURE 486-4 A. Human disease network. Each node corresponds to a specific disorder colored by class (22 classes, shown in the key to B). The size of each node is

proportional to the number of genes contributing to the disorder. Edges between disorders in the same disorder class are colored with the same (lighter) color, and edges

connecting different disorder classes are colored gray, with the thickness of the edge proportional to the number of genes shared by the disorders connected by it.

B. Disease gene network. Each node is a single gene, and any two genes are connected if implicated in the same disorder. In this network map, the size of each node is

proportional to the number of specific disorders in which the gene is implicated. (Reproduced with permission from KI Goh et al: The human disease network. Proc Natl

Acad Sci USA 104:8685, 2007. Copyright (2007) National Academy of Sciences, U.S.A.)

3818 PART 20 Frontiers

Topologic module Functional module

Topologically close genes (or products)

Functionally similar genes (or products)

Disease genes (or products)

Undirectional interactions

Directional interactions

Disease module

FIGURE 486-5 The elements of the interactome. The interactome includes topologic modules (genes or gene products that are closely associated with one another through

direct interactions), functional modules (genes or gene products that work together to define a pathway), and disease modules (genes or gene products that interact to

yield a pathophenotype). (Reproduced with permission from AL Barabási, N Gulbahce, J Loscalzo. Network medicine: A network-based approach to human disease. Nat

Rev Genet 12:56, 2011.)

Disease1 module Disease2 module

Potential sources:

(i) OMIM

(ii) GWAS

(iii) Literature

i. Interactome reconstruction iii. Disease module identification

ii. Disease gene (seed)

 identification

iv. Pathway identification v. Validation/prediction

Disease1

protein

Disease2

protein

Overlapping

protein

Known disease2 protein

Predicted disease2 protein

Prediction Validation

Functional homogeneity

Dynamic homogeneity

(i) Gene ontology

(ii) Tissue specificity

(iii) Phenotypic similarity

(i) Coexpression

(ii) Genetic interactions

(iii) Drug response

Disease genes

Disease pathways

Drug targets

FIGURE 486-6 Approaches to identifying disease modules in molecular networks. A strategy for defining disease modules involves (i) reconstructing the interactome; (ii)

ascertaining potential seed (disease) genes from the curated literature, the Online Mendelian Inheritance in Man (OMIM) database, or genomic analyses (genome-wide

association studies [GWAS] or transcriptional profiling); (iii) identifying the disease module using different modeling or statistical approaches; (iv) identifying pathways and

the role of disease genes or modules in those pathways; and (v) disease module validation and prediction. (Reproduced with permission from AL Barabási, N Gulbahce,

J Loscalzo. Network medicine: A network-based approach to human disease. Nat Rev Genet 12:56, 2011.)

TABLE 486-1 Examples of Systems Biology Application to Disease and Therapy

DISEASE ANALYSIS REFERENCE

Hereditary ataxias Many ataxia-causing proteins share interacting partners that affect

neurodegeneration

Lim et al: Cell 125:

801-814, 2006

Diabetes mellitus Metabolite-protein network analysis links three unique metabolite

abnormalities in prediabetics to seven type 2 diabetes genes through four

enzymes

Wang-Sattler et al: Mol Syst Biol 8:615, 2012

Epstein-Barr virus infection Viral proteome exerts its effects through linking to host interactome Gulbahce et al: PLoS One 8:e1002531, 2012

Pulmonary arterial hypertension Network analysis indicates adaptive role for microRNA 21 in suppressing rho

kinase pathway

Parikh et al: Circulation 125:1520-1532, 2012

Asthma Disease module in the interactome explains phenotype heterogeneity and

drug response

Sharma et al: Hum Mol Genet 24:3005-3020, 2015

Preeclampsia Network analysis demonstrates the central role of vitamin D signaling in

offsetting disease risk

Mirzakheni et al: J Clin Invest 126:4702-4715, 2016

Calcific aortic valve disease Network and systems biology approaches identify the first molecular

regulatory networks in vascular calcification

Schlotter et al: Circulation 138:377-393, 2018

Vascular fibrosis in pulmonary

arterial hypertension

Network analysis identifies novel scaffold protein NEDD9 as a key regulator

in pulmonary vascular fibrosis

Samokhin et al: Science Transl Med 10.1126/

scitranslmed.aap, 2018

Chronic obstructive pulmonary

disease

MicroRNA dysregulation identified from network analysis as a determinant

of vascular remodeling

Musri et al: Am J Respir Cell Mol Biol 59:490-499, 2018

Drug repurposing Network-based approach to in silico drug repurposing Cheng et al: Nat Commun 10:3476, 2019

3819Emerging Neurotherapeutic Technologies CHAPTER 487

shared disease mechanisms. Although it is unlikely that the extreme

of “individualized medicine” will ever be practical (or even desirable),

complex diseases can be mechanistically subclassified and interventions may be tailored to those settings in which they are more likely to

work. This approach serves as a basis for the development of precision

medicine.

■ FURTHER READING

Barabasi A-L et al: Network medicine: A network-based approach to

human disease. Nat Rev Genet 12:56, 2011.

Cheng F et al: Network-based approach to prediction and population-based validation of in silico drug repurposing. Nat Commun

9:2691, 2018.

Loscalzo J et al (eds): Network Medicine: Complex Systems in Human

Disease and Therapeutics. Cambridge, MA, Harvard University Press.

Copyright 2017 by the President and Fellows of Harvard College. All

rights reserved.

Loscalzo J et al: Human disease classification in the postgenomic era:

A complex systems approach to human pathobiology. Mol Syst Biol

3:124, 2007.

Menche J et al: Disease networks. Uncovering disease-disease relationships through the incomplete interactome. Science 347:1257601,

2015.

Oldham WM et al: Network analysis to risk stratify patients with exercise intolerance. Circ Res 122:864, 2018.

INTRODUCTION

Neurotherapeutic technologies represent a diverse group of very

promising treatment approaches with a common purpose of improving

neurologic function. Decades of basic science research have paved the

path for these novel technologies that have the potential to transform

the lives of patients with neurologic diseases. A key goal is to minimize

the consequences of lost abilities, whether they are motor, sensory, or

cognitive. A common objective is to also harness the inherent plasticity of the nervous system, regardless of age, and even in the face of a

degenerative process.

The technologies described below are the culimination of both an

increased understanding of neural plasticity mechanisms in both the

intact and the injured nervous system as well as advances in technology

and computational power. There has been important progress in understanding neural plasticity at the level of the microscale (e.g., cellular

and molecular processes), the mesoscale (e.g., between distinct cortical

and subcortical areas), and the macroscale (e.g., at the level of brain

neworks). While it is also clear that there may be fundamental limits on

plasticity—the closing of developmental windows—and repair mechanisms, the brain remains highly plastic regardless of age and even in the

face of ongoing injury and/or degenerative processes. Collectively, there

is now growing evidence to support neurologic restorative efforts for

both “static” (e.g., stroke) and progressive neurologic disorders.

These technologies may not appear, at first glance, directly relevant

to traditional medical care, but it is worth noting that clinicians

have the most knowledge and experience about the specific disease

process, the treatments available, and the expected course of illnesses

affecting the nervous system. It is thus critical that neurologic specialists and other clinicians play an important role in the future adoption

of these technologies for neurologic rehabilitation. The sections below

outline emerging diagnostic and therapeutic approaches that have the

487 Emerging Neurotherapeutic

Technologies

Jyoti Mishra, Karunesh Ganguly

potential to transform the lives of patients with neurologic disorders.

These include technologies to harness plasticity, neuroimaging, neurostimulation, and brain–machine interfaces.

TECHNOLOGIES TO HARNESS PLASTICITY

Neurologic rehabilitation aims to harness activity-dependent plasticity

mechanisms to maximize functional restoration. This principle can be

applied to a diverse range of functional domains such as movement

control, sensory processing, language, pain, and cognition. For example, recent randomized controlled clinical trials for motor recovery

after stroke have suggested that intensity of training may be particularly important for sustained long-term improvements. Moreover,

studies of the effects of such training in rodent and nonhuman primate

models further suggest that plasticity of cortical “motor maps” as well

as the coordinated firing of neurons in remaining networks underlie

observed functional improvements with rehabilitation. The incorporation of technology for neurologic rehabilitation has the great potential

to revolutionize the delivery of care by significantly increasing access,

reducing the burden for adherence to high-intensity regimens, and by

maximizing engagement. Below are three examples of how emerging

technology can be used to harness neural plasticity and to maximize

functional restoration.

■ ROBOTICS

Rehabilitation robotics for both the upper and the lower limb have

the potential to improve motor outcomes after stroke or other forms

of brain injury. There is a growing recognition that focused training

involving a range of tasks might be important for improved functional

outcomes. While it remains unclear exactly when such training might

be optimal after the initial stroke and during the early recovery period,

such training likely has a role in both the acute and the chronic periods after stroke; maintenance therapy may also guard against known

declines in function over time. Notably, the delivery of intensive

training is a great challenge from both the perspective of the health

care system and each patient. Outside of clinical trials, such a training

program can be quite difficult to implement and maintain. It can also

be costly and require significant effort.

Motor rehabilitation using robotics has been developed and tested

for both the upper limb and the lower limb. Such robotic therapies have

often focused on the delivery of high-intensity movement practice that

can surpass what is possible via existing standards of care. Moreover,

robotic systems are capable of precisely measuring movement parameters (e.g., the kinematics of the movements) and providing quantitative

feedback regarding the changes in performance during the training

period. A particular focus has been on maximal patient engagement

and recruitment of attentional and reward pathways, both of which are

increasingly recognized to drive neural plasticity. Continued advances

in design and the user interface will ensure maximal comfort and sustained effort. For example, via close monitoring of performance and

movement parameters, the system can provide assistance at key points

in order to minimize fatigue and to ensure maximal engagement.

Moreover, antigravity support of the upper limb can allow practice and

task engagement even in the presence of severe weakness; this would

be extremely challenging and labor intensive under current standards

of care. Recent analysis also suggests that robotic devices may at least

match outcomes realized with existing standards of care. However,

rehabilitation robotics may also provide more precise feedback and

permit novel quantitative rehabilitation approaches.

Figure 487-1 shows one example of an upper-limb robotic exoskeleton device that is currently being evaluated for training after

stroke. A recent randomized, multicenter trial compared treatment

with this exoskeleton system against conventional therapy provided

by physical and occupational therapists. Participants were enrolled

in the chronic phase and all had moderate-to-severe deficits; the

groups underwent three sessions per week over an 8-week period.

For robotic training, subjects trained with games to improve mobilization and to practice activities of daily living. This study provided

evidence that both conventional and robotic therapy could improve

function in patients with chronic stroke. Multiple studies have also

3820 PART 20 Frontiers

group of patients with neurologic impairments in the motor, sensory,

or cognitive domains. AR may offer a particularly unique rehabilitation

intervention for stroke patients. It is widely known that brain injuries

limit patients’ physical interaction with their environments. Furthermore, physical and cognitive impairments may limit social interactions. Such impoverished experiences are likely to be present during

both the acute and the chronic phases. Importantly, there is clear basic

scientific evidence that environmental enrichment can be a key component of rehabilitation; such enrichment may offer additive benefits

to the often-limited formal rehabilitation sessions per week. Consistent

with this are clinical studies suggesting that motor and cognitive outcomes may suffer when interactions with the environment are reduced;

AR may be capable of increasing enrichment. For example, in the case

of spatial neglect after stroke, the impaired modality may be accounted

for using AR methods. Similarly, physical impairments that limit walking speeds can also limit visual feedback; both AR and VR can be used

to enhance visual feedback during gait training.

Figure 487-2 shows a recent innovative application of AR for the

treatment of “phantom limb” pain. A subset of both upper-limb and

lower-limb amputees experiences painful sensations that appear to

originate from the missing limb. Past research has suggested that

mirror therapy can be an effective treatment for phantom limb pain.

During mirror therapy treatments, patients move their healthy arm in

front of a mirror in order to produce a perception of movements of the

missing limb. Previous studies have suggested that maladaptive plasticity of affected sensory cortices may be treated with mirror therapy.

Importantly, in comparison to mirror therapy, AR-based therapy for

phantom limb pain can be based on movements of the affected limb,

i.e., using the remaining portion of the limb as opposed to the unaffected contralateral limb. This study demonstrated a novel treatment

in which “phantom motor execution” is enabled using sophisticated

machine-learning algorithms. More specifically, the study “decoded”

phantom limb movements by measuring electromyogram (EMG)

activity at the stump. Importantly, while the distal muscles responsible

for movements were lost as a result of amputation, the remaining EMG

activity could be used to predict presumed distal limb movements. As

shown in Fig. 487-2, these inferred movements were projected onto

an AR screen to create the perception of limb movements. The study

showed that a subset of patients with long-term refractory phantom

limb pain could experience a significant reduction in pain levels after

using the AR system.

■ NEUROGAMING

Computerized programs that harness the power of video games have

shown some evidence for ameliorating deficits in visual perception,

age-related degeneration, and neuropsychiatric disorders. An essential

feature of effective video game training is the progressive adjustment

of the level of difficulty in line with the cognitive improvement of the

patient. Important areas of active research include ways to enhance sustainability of neurogame training over long time periods and improving training transfer, i.e., the generalizability of task-specific training in

one cognitive domain to more broad-based functional improvements.

By leveraging video game technology, neurogames allow for dynamic

user interaction and maintain user engagement across multiple sessions over several days of training. Important game mechanics include

repetitive practice, performance-adaptive challenges, and several layers of reward feedback—from moment-to-moment point rewards to

reward milestones over multiple sessions.

Notably, neurogames have therapeutic potential as they can be

targeted to specific neurocognitive deficits. For instance, games have

shown significant benefits in aging, by targeting speed of processing

and training the abilities to multitask and suppress distractions. In each

case, selective targeting is achieved by focusing the adaptive challenges

to the neurocognitive domain of interest. Duration of response time

windows available to the user or the level of interference are selectively

targeted in the case of speed of processing training and interference

training, respectively. More recent research demonstrated that it is

possible to engender focused circuit neuroplasticity using such selective targeting in neurogaming. For example, older adults learned to

FIGURE 487-1 Photograph of a subject interacting with a complex upper-limb

exoskeleton and a virtual reality system. (From U Keller et al: Robot-Assisted Arm

Assessments in Spinal Cord Injured Patients: A Consideration of Concept Study.

PLoS One 10:e0126948, 2015.)

found similar gains when using either conventional or traditional

approaches. Thus, a growing body of research supports the idea that

such devices might complement conventional approaches to rehabilitation. Future work will need to define how rehabilitation robotics

can optimally use adaptive and quantitative methods to further augment the recovery process.

■ VIRTUAL AND AUGMENTED REALITY

Therapeutic approaches using virtual reality (VR) and augmented

reality (AR) aim to treat neurologic illnesses by specifically and quantitatively altering a patient’s subjective experiences and interactions

with the environment. Core components of both are advanced hardware and computational methods to generate simulated, yet realistic,

perceptions. While some applications permit users to dynamically

change the viewed perspective, other applications are designed to allow

interactions among multiple users. Visual feedback is often a key component; this can include simple computer monitors or more immersive

“head-mounted” viewers that modify the simulation based on changes

in perspective. Tracking of movements (e.g., hand and head position)

is often included. Multiple methods are used to allow a user to interact

with the environment; interactions can be guided by straightforward

means such as a keyboard, mouse, or even a joystick. More immersive

methods are also frequently used. For example, gloves with embedded

sensors and haptic inputs can allow the user’s hand to be represented

in real time in the simulated environment. Moreover, haptic interfaces

can provide sensory feedback, allowing patients to interact with and

“feel” virtual objects through multiple sensory modalities. A particular

strength of these approaches is that therapeutic interventions can be

studied in very controlled environments.

VR enables a user to interact with a simulated reality that can be precisely and quantitatively controlled. In addition to allowing patients to

dynamically experience an altered reality, it can simultaneously monitor a subject’s behaviors and responses. Such monitoring can allow

precise measurements of clinically relevant parameters (e.g., motor

actions, perception, cognitive processing) and can also be applied in

specific rehabilitation training to achieve functionally meaningful

goals. A growing body of literature indicates that VR environments can

be tailored to individual needs and preferences, thereby maximizing

engagement, motivation, and adaptation to ensure sufficient difficulty

of tasks. VR environments can be designed to create powerful “gaming”

platforms that are actually targeting clinically relevant parameters. For

example, the upper-limb robotic systems described previously are frequently combined with VR environments that allow interaction with

virtual objects.

In contrast to VR, AR overlays an artificial filter over a subject’s

view of the actual physical world, thus providing an “augmented” or

enhanced view of the world around. AR is being tested in a diverse