Aging brain refers to biological and functional changes that occur in the brain
as individuals advance in age. It encompasses both the normal
alterations which are universally experienced and abnormalities induced
by illnesses, diagnosed or not. The concept is most often used in
relation to humans.
Since life extension is only pertinent if accompanied by healthspan
extension, and, more importantly, by preserving brain health and
cognition, finding rejuvenating approaches that act simultaneously in
peripheral tissues and in brain function is a key strategy in the
development of rejuvenation technology.
Aging entails many physical, biological, chemical, and
psychological changes and the brain is no exception to this phenomenon.
These various changes have attempted to be mapped by conceptual models like the Scaffolding Theory of Aging and Cognition (STAC) in 2009. The STAC model looks at factors like neural changes to the white matter, dopamine depletion, shrinkage, and cortical thinning. 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. 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. 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. 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. White matter consists of tightly packed myelinatedaxons connecting the neurons to each other and with the periphery.
Loss of neural circuits and brain plasticity
Brain plasticity refers to the brain's ability to change structure and function. This ties into the common phrase, "if you don't use it, you lose it,"
which is another way of saying, "if you do not 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. The changes in the organism's 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. 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.
Advances in MRI technology have provided the ability to see the brain structure in great detail in an easy, non-invasive manner in vivo. 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. 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. 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. 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. It has also been found that the width of sulcus not only increases with age, but also with cognitive decline in the elderly.
Morphology and microstructure
Age-related decrease in gray matter volume was the largest contribution to changes in brain volume. Moreover, neuronal density appears to decrease, white matter microstructure gets altered and energy metabolism in the cerebellum gets altered. General cortical atrophy occurs in aging and e.g. the caudate nucleus volume appears to decrease.
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. Studies by Duan et al., have shown that dendritic arbors and dendritic spines of corticalpyramidal 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). A 46% decrease in spine number and spine density has been
reported in humans older than 50 compared with younger individuals. 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).
Age-related neuropathologies such as Alzheimer's disease, Parkinson's disease, diabetes, hypertension and arteriosclerosis make it difficult to distinguish the normal patterns of aging.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). In normal, non-demented aging, the number of tangles in each affected cell body is relatively low and restricted to the olfactory nucleus, parahippocampal gyrus, amygdala and entorhinal cortex. 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.
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.
Cognitive impairment has been attributed to oxidative stress, inflammatory reactions and changes in the cerebral microvasculature. 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 held to be associated with aging." 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. 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. Oxidative stress can damage DNA replication and inhibit repair through many complex processes, including telomere shortening in DNA components. Each time a somatic cell replicates, the telomeric DNA component shortens. As telomere length is partly inheritable, 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 cross-links and malondialdehydeadducts (reviewed in Bernstein et al.).
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.
Lu et al. 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.
Cerebral blood flow was shown to decrease 0.3-0.5% per year in healthy ageing. An efficiently functioning glymphatic system,
involved in waste clearance, may be important for maintaining brain
health and its transport efficiency appears to be declining with aging. Factors in the circulation have been shown to modulate ageing and to rejuvenate the brain.
Chemical changes
Major dopamine pathways. As part of the reward pathway, dopamine is manufactured in nerve cell bodies located within VTA and is released in the nucleus accumbens and the prefrontal cortex. The motor functions of dopamine are linked to a separate pathway, with cell bodies in the substantia nigra that manufacture and release dopamine into the striatum.Dopamine and serotonin functions and pathways
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, notably in the striatum and extrastriatal regions (excluding the midbrain). Significant age-related decreases in dopamine receptors D1, D2, and D3 have also been highly reported. A general decrease in D1 and D2 receptors has been shown, and more specifically a decrease of D1 and D2 receptor binding in the caudate nucleus and putamen.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. One study also indicated a significant inverse correlation between dopamine binding in the occipital cortex and age. 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. 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. Changes in dopamine levels may also cause age-related changes in cognitive flexibility.
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. A decreased binding capacity of the 5-HT2 receptor in the frontal cortex was also found, as well as a decreased binding capacity of the serotonin transporter (5-HHT) in the thalamus and the midbrain. 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.
Glutamate
Expression of glutamate transporter 1 in glial cell facilitates reuptake of glutamate and decreases extracellular glutamate concentration
Glutamate is another neurotransmitter that tends to decrease with age. Studies have shown older subjects to have lower glutamate concentration in the motor cortex compared to younger subjects. 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. 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.
Neuropsychological changes
Changes in orientation
Orientation is defined as the awareness of self in relation to one's surroundings. 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. 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. For example, in one study 92% of normal elderly adults (65–84 years) presented with perfect or near perfect orientation. However some data suggest that mild changes in orientation may be a normal part of aging. 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." 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 a particular
pathology.
Changes in attention
Many older adults notice a decline in their attentional abilities. 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". 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. 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 to the middle of the eighth decade
of life. 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.
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.
Behavioral flexibility can refer to efficiently and
appropriately adapting to different situations and changing
environmental demands, including the speed of adaptation, and to the
capacity to develop solutions to novel problems or novel solutions to
old problems. Studies indicate late-stage aging, and/or late-life dementias, decreases behavioral flexibility and impair deliberation about courses of action.
Variation in the effects of aging among individuals can be attributed to both genetic, health, and environmental factors. As in so many other science disciplines, the nature versus nurture debate is an ongoing conflict in the field of cognitive neuroscience. The search for genetic factors has always been an important aspect in trying to understand neuropathological
processes. Research focused on discovering the genetic component in
developing Autosomal Dominant (AD) has also contributed greatly to the
understanding the genetics behind normal or "non-pathological" aging.
Autosomal Dominant (AD) - Autosomal dominant is a pattern of inheritance characteristic of some genetic disorders. "Autosomal" means that the gene in question is located on one of the numbered, or non-sex, chromosomes. "Dominant" means that a single copy of the mutated gene (from one parent) is enough to cause the disorder.
The human brain shows a decline in function and a change in gene expression. This modulation in gene expression may be due to oxidativeDNA damage at promoter regions in the genome. Genes that are down-regulated over the age of 40 include:
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. By contrast, all brain regions and brain cells appear to have roughly
the same epigenetic age in subjects who are younger than 80. These findings suggest that the cerebellum is better 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.
The current state of biomedical technology does not allow to stop and reverse aging. However, one may potentially delay the effects and severity of its symptoms.
While there is no consensus of efficacy, the following are reported as delaying cognitive decline:
The microbiome also plays a role. Scientists have shown that transplantation of fecal microbiota from young donor mice into aged recipient mice substantially rejuvenates brain biomarkers of the latter, complementing similar results of a 2020 study. Diet and other factors influence the microbiome. Probiotics such as of L. plantarum may also have relevant effects.
The ability of an individual to demonstrate attenuated
cognitive signs of aging despite an aging brain is called cognitive
reserve. 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 some people more resistant to
cognitive decline than others.
Intellectual quotients derived from psychometric testing
have been identified as valuable proxy measures of cognitive reserve,
with higher scores relative to the mean being associated with slower
rates of cognitive decline. However, the rate of decline in some cognitive subdomains, such as processing speed, may be less affected by premorbid IQ. The degree of association between IQ and cognitive reserve may vary between different types of dementia.
Research
"Super Agers"
Longitudinal research studies have recently conducted genetic analyses of centenarians and their offspring to identify protective factors against the negative effects of aging. In particular, the CETP gene is linked to prevention of cognitive decline and Alzheimer's disease. 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.
A study funded by the National Institute on Aging
(NIA) began in 1986 and 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 cerebral atrophy, and more neurofibrillary tangles.
In 1994, Religious Orders Study has begun. Its initial funding was also provided by NIA.
A study showed that infusing the nourishing cerebrospinal fluid
from around brain cells of young mice into aged brains rejuvenates
aspects of the brain, proving that it play a role in brain aging and inter alia identifying a protein FGF17 as a key target for potential therapeutics, including for anti-aging.
The subarachnoidal lymphatic-like membrane,
whose discovery was reported around 2023, likely plays a role in
cerebrospinal fluid functions and, as both a protective barrier and a
host of immune cells that monitor the brain for infection and inflammation, appears to be substantially involved in major brain diseases and brain aging. It is "the host for a large population of myeloid cells [(see above)], the number of which increases in response to inflammation and aging".
Aging disparities
For certain demographics, the effects of normal cognitive
aging are especially pronounced. Differences in cognitive aging might be
tied to the lack of or reduced access to medical care and, as a result,
suffer disproportionately from negative health outcomes. As the global
population grows, diversifies, and grays, there is an increasing need to
understand these inequities.
Race
African Americans
Life expectancy in the US by raceLife expectancy in the US by race and sex, with calculated sex gapProbability of dying at various ages in the US in 2019 by race and sex. See also male-only and female-only versions of the chart.
In the United States, Black and African American demographics disproportionately experience metabolic dysfunction with age. This has many downstream effects, but the most prominent of these is the toll on cardiovascular health. Metabolite
profiles of the healthy aging index - a score that assesses
neurocognitive function, among other correlates of health through the
years - are associated with cardiovascular disease. Healthy cardiovascular function is critical for maintaining neurocognitive efficiency into old age. Attention, verbal learning, and cognitive set ability are related to diastolic blood pressure, triglyceride levels, and HDL cholesterol levels, respectively.
Among different Latin heritages, frequency of the dementia-predisposing ε4 allele of apoE4 gene was highest for Caribbean Latinos (Cubans, Dominicans, Puerto Ricans, 12.6–17.5%) and lowest among mainland Latinos (Mexicans, Central Americans, and South Americans, 11.0–11.2%). At the same time, frequency of the neuroprotective ε2 allele was also highest for Caribbean Latinos (5.2–8.6%) and lowest for those of mainland heritage (2.9–3.9%). Among mainland Latinos, the most prevalent is the "median" ε3 allele: 85.2–86.2% compared to 73.9–81.5% among Caribbean Latinos.
Indigenous peoples
Indigenous populations are often understudied in research.
Reviews of current literature studying natives in Australia, Brazil,
Canada, and the United States from participants aged 45 to 94 years old
reveal varied prevalence rates for cognitive impairment not related to
dementia, from 4.4% to 17.7%. These results can be interpreted in the context of culturally biased
neurocognitive tests, preexisting health conditions, poor access to
healthcare, lower educational attainment, and/or old age.
Sex
Compared to their male counterparts, women's scores on the mini–mental state examination (MMSE) tend to decline at slightly faster rates with age. Males with mild cognitive impairment tend to show more microstructural damage than females with MCI, but seem to have a greater cognitive reserve
due to larger absolute brain size and neuronal density. As a result,
women tend to manifest symptoms of cognitive decline at lower thresholds
than men do. This effect seems to be moderated by educational attainment - higher education is associated with later diagnosis of mild cognitive impairment as neuropathological load increases. Currently there are no known studies to identify a characteristic pattern of cognitive decline with age in transgender people.
Socioeconomic factors
Socioeconomic status
is the interaction between social and economic factors. It has been
demonstrated that socio-demographic factors can be used to predict
cognitive profiles within older individuals to some extent. This may be because families of higher socioeconomic status (SES) are
equipped to provide their children with resources early on to facilitate
cognitive development. For children in families of low SES, relatively
small changes in parental income were associated with large changes in
brain surface area; these losses were seen in areas associated with
language, reading, executive functions,
and spatial skills. Meanwhile, for children in families of high SES,
small changes in parental income were associated with small changes in
surface area within these regions. With respect to global cortical thickness, low SES children showed a
curvilinear decrease in thickness with age while those of high SES
demonstrated a steeper linear decline, suggesting that synaptic pruning is more efficient in the latter group. This trend was especially evident in the left fusiform and left superior temporal gyri - critical language and literacy supporting areas.
A study showed that 50+ aged users of the dietary program SNAP
"had about 2 fewer years of cognitive aging over a 10-year period
compared with non-users" despite it having nearly no conditions for the sustainability and healthiness of the food products purchased with the coupons (or coupon-credits).
An fMRI image with yellow areas showing increased activity compared with a control condition
Purpose
Measures brain activity detecting changes due to blood flow.
Functional magnetic resonance imaging or functional MRI (fMRI) measures brain activity by detecting changes associated with blood flow. This technique relies on the fact that cerebral blood flow and neuronal
activation are coupled: When an area of the brain is in use, blood flow
to that region increases.
The primary form of fMRI uses the blood-oxygen-level dependent (BOLD) contrast, discovered by Seiji Ogawa and his colleagues in 1990. This is a type of specialized brain and body scan used to map neural activity in the brain or spinal cord of humans or other animals by imaging the change in blood flow (hemodynamic response) related to energy use by nerve cells. Since the early 1990s, fMRI has come to dominate brain mapping
research because it is noninvasive, typically requiring no injections,
surgery, or the ingestion of substances such as radioactive tracers as
in positron emission tomography. Data acquired using fMRI, however, is frequently corrupted by noise
from various sources; hence, statistical procedures are used to extract
the underlying signal. The resulting brain activation can be graphically
represented by color-coding the strength of activation across the brain
or the specific region studied. The technique can localize activity to
within millimeters but, using standard techniques, no better than within
a window of a few seconds. Other methods of obtaining contrast are arterial spin labeling and diffusion MRI.
Diffusion MRI is similar to BOLD fMRI but provides contrast based on
the magnitude of diffusion of water molecules in the brain.
In addition to detecting BOLD responses from activity due to tasks or stimuli, fMRI can measure resting state,
or negative-task state, which shows the subjects' baseline BOLD
variance. Since about 1998 studies have shown the existence and
properties of the default mode network, a functionally connected neural network of apparent resting brain states.
fMRI is used in research, and to a lesser extent, in
clinical work. It can complement other measures of brain physiology such
as electroencephalography (EEG), and near-infrared spectroscopy
(NIRS). Newer methods which improve both spatial and time resolution
are being researched, and these largely use biomarkers other than the
BOLD signal. Some companies have developed commercial products such as
lie detectors based on fMRI techniques, but the research is not believed
to be developed enough for widespread commercial use.
Overview
fMRI
is a non-invasive technique that uses magnetic fields to create images
highlighting changes in the metabolic processes in the body (usually the
brain) by measuring the changes in blood flow.
The fMRI concept builds on the earlier MRI
scanning technology and the discovery of properties of oxygen-rich
blood. MRI brain scans use a strong, uniform, static magnetic field to
align the spins of nuclei in the brain region being studied. Another
magnetic field, with a gradient strength rather than a uniform one, is
then applied to spatially distinguish different nuclei. Finally, a
radiofrequency (RF) pulse is applied to flip the nuclear spins, with the
effect depending on where they are located, due to the gradient field.
After the RF pulse, the nuclei return to their original (equilibrium)
spin populations, and the energy they emit is measured with a coil. The
use of the gradient field allows the positions of the nuclei to be
determined. MRI thus provides a static structural view of brain matter.
The central thrust behind fMRI was to extend MRI to capture functional
changes in the brain caused by neuronal activity. Differences in
magnetic properties between arterial (oxygen-rich) and venous
(oxygen-poor) blood provided this link.
Researcher checking fMRI images
Since the 1890s, it has been known that changes in blood flow and blood oxygenation in the brain (collectively known as brain hemodynamics) are closely linked to neural activity. When neurons become active, local blood flow to those brain regions
increases, and oxygen-rich (oxygenated) blood displaces oxygen-depleted
(deoxygenated) blood around 2 seconds later. This rises to a peak over
4–6 seconds, before falling back to the original level (and typically
undershooting slightly). Oxygen is carried by the hemoglobin molecule in red blood cells. Deoxygenated hemoglobin (dHb) is more magnetic (paramagnetic) than oxygenated hemoglobin (Hb), which is virtually resistant to magnetism (diamagnetic).
This difference leads to an improved MR signal since the diamagnetic
blood interferes with the magnetic MR signal less. This improvement can
be mapped to show which neurons are active at a time.
History
During the late 19th century, Angelo Mosso invented the 'human circulation balance', which could non-invasively measure the redistribution of blood during emotional and intellectual activity. However, although briefly mentioned by William James in 1890, the details and precise workings of this balance and the experiments
Mosso performed with it remained largely unknown until the recent
discovery of the original instrument as well as Mosso's reports by Stefano Sandrone and colleagues. Angelo Mosso investigated several critical variables that are still relevant in modern neuroimaging such as the 'signal-to-noise ratio', the appropriate choice of the experimental paradigm and the need for the simultaneous recording of differing physiological parameters. Mosso's manuscripts do not provide direct evidence that the balance
was really able to measure changes in cerebral blood flow due to cognition, however a modern replication performed by David T Field has now demonstrated—using modern signal processing techniques
unavailable to Mosso—that a balance apparatus of this type is able to
detect changes in cerebral blood volume related to cognition.
In 1890, Charles Roy and Charles Sherrington first experimentally linked brain function to its blood flow, at Cambridge University. The next step to resolving how to measure blood flow to the brain was Linus Pauling's
and Charles Coryell's discovery in 1936 that oxygen-rich blood with Hb
was weakly repelled by magnetic fields, while oxygen-depleted blood with
dHb was attracted to a magnetic field, though less so than ferromagnetic elements such as iron. Seiji Ogawa at AT&T Bell labs
recognized that this could be used to augment MRI, which could study
just the static structure of the brain, since the differing magnetic
properties of dHb and Hb caused by blood flow to activated brain regions
would cause measurable changes in the MRI signal. BOLD is the MRI
contrast of dHb, discovered in 1990 by Ogawa. In a seminal 1990 study
based on earlier work by Thulborn et al., Ogawa and colleagues scanned
rodents in a strong magnetic field (7.0T)
MRI. To manipulate blood oxygen level, they changed the proportion of
oxygen the animals breathed. As this proportion fell, a map of blood
flow in the brain was seen in the MRI. They verified this by placing
test tubes with oxygenated or deoxygenated blood and creating separate
images. They also showed that gradient-echo images, which depend on a
form of loss of magnetization called T2*
decay, produced the best images. To show these blood flow changes were
related to functional brain activity, they changed the composition of
the air breathed by rats, and scanned them while monitoring brain
activity with EEG. The first attempt to detect the regional brain activity using MRI was performed by Belliveau and colleagues at Harvard University using the contrast agent
Magnevist, a paramagnetic substance remaining in the bloodstream after
intravenous injection. However, this method is not popular in human
fMRI, because of the inconvenience of the contrast agent injection, and
because the agent stays in the blood only for a short time.
Three studies in 1992 were the first to explore using the BOLD contrast in humans. Kenneth Kwong and colleagues, using both gradient-echo and inversion recovery echo-planar imaging (EPI) sequence at a magnetic field strength of 1.5 T published studies showing clear activation of the human visual cortex. The Harvard team thereby showed that both blood flow and blood volume
increased locally in activity neural tissue. Ogawa and Ugurbil conducted
a similar study using a higher magnetic field (4.0 T) in Ugurbil's
laboratory at the University of Minnesota,
generating higher resolution images that showed activity largely
following the gray matter of the brain, as would be expected; in
addition, they showed that fMRI signal depended on a decrease in T2*,
consistent with the BOLD mechanism. T2* decay is caused by magnetized
nuclei in a volume of space losing magnetic coherence (transverse
magnetization) from both bumping into one another and from experiencing
differences in the magnetic field strength across locations (field
inhomogeneity from a spatial gradient). Bandettini and colleagues used
EPI at 1.5 T to show activation in the primary motor cortex, a brain
area at the last stage of the circuitry controlling voluntary movements.
The magnetic fields, pulse sequences and procedures and techniques used
by these early studies are still used in current-day fMRI studies. But
today researchers typically collect data from more slices (using
stronger magnetic gradients), and preprocess and analyze data using
statistical techniques.
Physiology
The brain does not store a lot of glucose, its primary
source of energy. When neurons become active, getting them back to their
original state of polarization requires actively pumping ions across
the neuronal cell membranes, in both directions. The energy for those ion pumps
is mainly produced from glucose. More blood flows in to transport more
glucose, also bringing in more oxygen in the form of oxygenated
hemoglobin molecules in red blood cells. This is from both a higher rate
of blood flow and an expansion of blood vessels. The blood-flow change
is localized to within 2 or 3mm
of where the neural activity is. Usually the brought-in oxygen is more
than the oxygen consumed in burning glucose (it is not yet settled
whether most glucose consumption is oxidative), and this causes a net
decrease in deoxygenated hemoglobin (dHb) in that brain area's blood
vessels. This changes the magnetic property of the blood, making it
interfere less with the magnetization and its eventual decay induced by
the MRI process.
The cerebral blood flow (CBF) corresponds to the consumed
glucose differently in different brain regions. Initial results show
there is more inflow than consumption of glucose in regions such as the amygdala, basal ganglia, thalamus and cingulate cortex, all of which are recruited for fast responses. In regions that are more deliberative, such as the lateral frontal and lateral parietal lobes, it seems that incoming flow is less than consumption. This affects BOLD sensitivity.
Hemoglobin differs in how it responds to magnetic fields,
depending on whether it has a bound oxygen molecule. The dHb molecule is
more attracted to magnetic fields. Hence, it distorts the surrounding
magnetic field induced by an MRI scanner, causing the nuclei there to
lose magnetization faster via the T2* decay. Thus MR pulse sequences sensitive to T2*
show more MR signal where blood is highly oxygenated and less where it
is not. This effect increases with the square of the strength of the
magnetic field. The fMRI signal hence needs both a strong magnetic field
(1.5T or higher) and a pulse sequence such as EPI, which is sensitive to T2* contrast.
The physiological blood-flow response largely decides the
temporal sensitivity, that is how accurately we can measure when neurons
are active, in BOLD fMRI. The basic time resolution parameter (sampling
time) is designated TR; the TR dictates how often a particular brain
slice is excited and allowed to lose its magnetization. TRs could vary
from the very short (500ms) to the very long (3s).
For fMRI specifically, the hemodynamic response lasts over 10 seconds,
rising multiplicatively (that is, as a proportion of current value),
peaking at 4 to 6 seconds, and then falling multiplicatively. Changes in
the blood-flow system, the vascular system, integrate responses to
neuronal activity over time. Because this response is a smooth
continuous function, sampling with ever-faster TRs does not help; it
just gives more points on the response curve obtainable by simple linear
interpolation anyway. Experimental paradigms such as staggering when a
stimulus is presented at various trials can improve temporal resolution, but reduces the number of effective data points obtained.
BOLD hemodynamic response
Main brain functional imaging technique resolutions
The change in the MR signal from neuronal activity is called the hemodynamic response
(HR). It lags the neuronal events triggering it by a couple of seconds,
since it takes a while for the vascular system to respond to the
brain's need for glucose. From this point it typically rises to a peak
at about 5seconds after the
stimulus. If the neurons keep firing, say from a continuous stimulus,
the peak spreads to a flat plateau while the neurons stay active. After
activity stops, the BOLD signal falls below the original level, the
baseline, a phenomenon called the undershoot. Over time the signal
recovers to the baseline. There is some evidence that continuous
metabolic requirements in a brain region contribute to the undershoot.
The mechanism by which the neural system provides feedback
to the vascular system of its need for more glucose is partly the
release of glutamate as part of neuron firing. This glutamate affects nearby supporting cells, astrocytes, causing a change in calcium ion concentration. This, in turn, releases nitric oxide at the contact point of astrocytes and intermediate-sized blood vessels, the arterioles. Nitric oxide is a vasodilator causing arterioles to expand and draw in more blood.
A single voxel's
response signal over time is called its timecourse. Typically, the
unwanted signal, called the noise, from the scanner, random brain
activity and similar elements is as big as the signal itself. To
eliminate these, fMRI studies repeat a stimulus presentation multiple
times.
Spatial resolution
Spatial resolution of an fMRI study refers to how well it
discriminates between nearby locations. It is measured by the size of
voxels, as in MRI. A voxel is a three-dimensional rectangular cuboid,
whose dimensions are set by the slice thickness, the area of a slice,
and the grid imposed on the slice by the scanning process. Full-brain
studies use larger voxels, while those that focus on specific regions of
interest typically use smaller sizes. Sizes range from 4 to 5mm, or with laminar resolution fMRI (lfMRI), to submillimeter. Smaller voxels contain fewer neurons on average, incorporate less blood
flow, and hence have less signal than larger voxels. Smaller voxels
imply longer scanning times, since scanning time directly rises with the
number of voxels per slice and the number of slices. This can lead both
to discomfort for the subject inside the scanner and to loss of the
magnetization signal. A voxel typically contains a few million neurons
and tens of billions of synapses, with the actual number depending on voxel size and the area of the brain being imaged.
The vascular arterial system supplying fresh blood
branches into smaller and smaller vessels as it enters the brain surface
and within-brain regions, culminating in a connected capillary bed within the brain. The drainage system, similarly, merges into larger and larger veins
as it carries away oxygen-depleted blood. The dHb contribution to the
fMRI signal is from both the capillaries near the area of activity and
larger draining veins that may be farther away. For good spatial
resolution, the signal from the large veins needs to be suppressed,
since it does not correspond to the area where the neural activity is.
This can be achieved either by using strong static magnetic fields or by
using spin-echo pulse sequences. With these, fMRI can examine a spatial range from millimeters to centimeters, and can hence identify Brodmann areas (centimeters), subcortical nuclei such as the caudate, putamen and thalamus, and hippocampal subfields such as the combined dentate gyrus/CA3, CA1, and subiculum.
Temporal resolution
Temporal resolution is the smallest time period of neural
activity reliably separated out by fMRI. One element deciding this is
the sampling time, the TR. Below a TR of 1 or 2seconds,
however, scanning just generates sharper hemodynamic response (HR)
curves, without adding much additional information (e.g. beyond what is
alternatively achieved by mathematically interpolating the curve gaps at
a lower TR). Temporal resolution can be improved by staggering stimulus
presentation across trials. If one-third of data trials are sampled
normally, one-third at 1s, 4s, 7s and so on, and the last third at 2s, 5s and 8s, the combined data provide a resolution of 1s, though with only one-third as many total events.
The time resolution needed depends on brain processing
time for various events. An example of the broad range here is given by
the visual processing system. What the eye sees is registered on the
photoreceptors of the retina within a millisecond or so. These signals
get to the primary visual cortex via the thalamus in tens of
milliseconds. Neuronal activity related to the act of seeing lasts for
more than 100ms. A fast reaction, such as swerving to avoid a car crash, takes around 200ms.
By about half a second, awareness and reflection of the incident sets
in. Remembering a similar event may take a few seconds, and emotional or
physiological changes such as fear arousal may last minutes or hours.
Learned changes, such as recognizing faces or scenes, may last days,
months, or years. Most fMRI experiments study brain processes lasting a
few seconds, with the study conducted over some tens of minutes.
Subjects may move their heads during that time, and this head motion
needs to be corrected for. So does drift in the baseline signal over
time. Boredom and learning may modify both subject behavior and
cognitive processes.
Linear addition from multiple activation
When a person performs two tasks simultaneously or in
overlapping fashion, the BOLD response is expected to add linearly. This
is a fundamental assumption of many fMRI studies that is based on the
principle that continuously differentiable systems can be expected to
behave linearly when perturbations are small; they are linear to first
order. Linear addition means the only operation allowed on the
individual responses before they are combined (added together) is a
separate scaling of each. Since scaling is just multiplication by a
constant number, this means an event that evokes, say, twice the neural
response as another, can be modeled as the first event presented twice
simultaneously. The HR for the doubled-event is then just double that of
the single event.
To the extent that the behavior is linear, the time course
of the BOLD response to an arbitrary stimulus can be modeled by
convolution of that stimulus with the impulse BOLD response. Accurate
time course modeling is important in estimating the BOLD response
magnitude.
This strong assumption was first studied in 1996 by
Boynton and colleagues, who checked the effects on the primary visual
cortex of patterns flickering 8times a second and presented for 3 to 24seconds.
Their result showed that when visual contrast of the image was
increased, the HR shape stayed the same but its amplitude increased
proportionally. With some exceptions, responses to longer stimuli could
also be inferred by adding together the responses for multiple shorter
stimuli summing to the same longer duration. In 1997, Dale and Buckner
tested whether individual events, rather than blocks of some duration,
also summed the same way, and found they did. But they also found
deviations from the linear model at time intervals less than 2seconds.
A source of nonlinearity in the fMRI response is from the
refractory period, where brain activity from a presented stimulus
suppresses further activity on a subsequent, similar, stimulus. As
stimuli become shorter, the refractory period becomes more noticeable.
The refractory period does not change with age, nor do the amplitudes of
HRs. The period differs across brain regions. In both the primary motor cortex
and the visual cortex, the HR amplitude scales linearly with duration
of a stimulus or response. In the corresponding secondary regions, the supplementary motor cortex,
which is involved in planning motor behavior, and the motion-sensitive
V5 region, a strong refractory period is seen and the HR amplitude stays
steady across a range of stimulus or response durations. The refractory
effect can be used in a way similar to habituation to see what features of a stimulus a person discriminates as new. Further limits to linearity exist because of saturation: with large stimulation levels a maximum BOLD response is reached.
Matching neural activity to the BOLD signal
Researchers have checked the BOLD signal against both
signals from implanted electrodes (mostly in monkeys) and signals of
field potentials (that is the electric or magnetic field from the
brain's activity, measured outside the skull) from EEG and MEG. The local field potential, which includes both post-neuron-synaptic activity and internal neuron processing, better predicts the BOLD signal. So the BOLD contrast reflects mainly the inputs to a neuron and the
neuron's integrative processing within its body, and less the output
firing of neurons. In humans, electrodes can be implanted only in
patients who need surgery as treatment, but evidence suggests a similar
relationship at least for the auditory cortex
and the primary visual cortex. Activation locations detected by BOLD
fMRI in cortical areas (brain surface regions) are known to tally with
CBF-based functional maps from PET scans. Some regions just a few millimeters in size, such as the lateral geniculate nucleus
(LGN) of the thalamus, which relays visual inputs from the retina to
the visual cortex, have been shown to generate the BOLD signal correctly
when presented with visual input. Nearby regions such as the pulvinar nucleus
were not stimulated for this task, indicating millimeter resolution for
the spatial extent of the BOLD response, at least in thalamic nuclei.
In the rat brain, single-whisker touch has been shown to elicit BOLD
signals from the somatosensory cortex.
However, the BOLD signal cannot separate feedback and
feedforward active networks in a region; the slowness of the vascular
response means the final signal is the summed version of the whole
region's network; blood flow is not discontinuous as the processing
proceeds. Also, both inhibitory and excitatory input to a neuron from
other neurons sum and contribute to the BOLD signal. Within a neuron
these two inputs might cancel out. The BOLD response can also be affected by a variety of factors,
including disease, sedation, anxiety, medications that dilate blood
vessels, and attention (neuromodulation).
The amplitude of the BOLD signal does not necessarily
affect its shape. A higher-amplitude signal may be seen for stronger
neural activity, but peaking at the same place as a weaker signal. Also,
the amplitude does not necessarily reflect behavioral performance. A
complex cognitive task may initially trigger high-amplitude signals
associated with good performance, but as the subject gets better at it,
the amplitude may decrease with performance staying the same. This is
expected to be due to increased efficiency in performing the task. The BOLD response across brain regions cannot be compared directly even
for the same task, since the density of neurons and the blood-supply
characteristics are not constant across the brain. However, the BOLD
response can often be compared across subjects for the same brain region
and the same task.
More recent characterization of the BOLD signal has used
optogenetic techniques in rodents to precisely control neuronal firing
while simultaneously monitoring the BOLD response using high field
magnets (a technique sometimes referred to as "optofMRI"). These techniques suggest that neuronal firing is well correlated with
the measured BOLD signal including approximately linear summation of the
BOLD signal over closely spaced bursts of neuronal firing. Linear summation is an assumption of commonly used event-related fMRI designs.
Medical use
Composite images from an fMRI scan
Physicians use fMRI to assess how risky brain surgery or
similar invasive treatment is for a patient and to learn how a normal,
diseased or injured brain is functioning. They map the brain with fMRI
to identify regions linked to critical functions such as speaking,
moving, sensing, or planning. This is useful to plan for surgery and
radiation therapy of the brain.
Clinical use of fMRI still lags behind research use. Patients with brain pathologies are more difficult to scan with fMRI
than are young healthy volunteers, the typical research-subject
population. Tumors and lesions can change the blood flow in ways not
related to neural activity, masking the neural HR. Drugs such as antihistamines and even caffeine can affect HR. Some patients may have disorders such as compulsive lying, which makes certain studies impossible. It is harder for those with clinical problems to stay still for long.
Using head restraints or bite bars may injure epileptics who have a
seizure inside the scanner; bite bars may also discomfort those with
dental prostheses.
Despite these difficulties, fMRI has been used clinically
to map functional areas, check left-right hemispherical asymmetry in
language and memory regions, check the neural correlates of a seizure,
study how the brain recovers partially from a stroke, and test how well a
drug or behavioral therapy
works. Mapping of functional areas and understanding lateralization of
language and memory help surgeons avoid removing critical brain regions
when they have to operate and remove brain tissue. This is of
particular importance in removing tumors and in patients who have
intractable temporal lobe
epilepsy. Lesioning tumors requires pre-surgical planning to ensure no
functionally useful tissue is removed needlessly. Recovered depressed
patients have shown altered fMRI activity in the cerebellum, and this
may indicate a tendency to relapse. Pharmacological fMRI, assaying brain
activity after drugs are administered, can be used to check how much a
drug penetrates the blood–brain barrier and dose vs effect information of the medication.
Animal research
Research is primarily performed in non-human primates such as the rhesus macaque.
These studies can be used both to check or predict human results and to
validate the fMRI technique itself. But the studies are difficult
because it is hard to motivate an animal to stay still and typical
inducements such as juice trigger head movement while the animal
swallows it. It is also expensive to maintain a colony of larger animals
such as the macaque.
Analyzing the data
The goal of fMRI data analysis is to detect correlations
between brain activation and a task the subject performs during the
scan. It also aims to discover correlations with the specific cognitive
states, such as memory and recognition, induced in the subject. The BOLD signature of activation is relatively weak, however, so other
sources of noise in the acquired data must be carefully controlled. This
means that a series of processing steps must be performed on the
acquired images before the actual statistical search for task-related
activation can begin. Nevertheless, it is possible to predict, for example, the emotions a
person is experiencing solely from their fMRI, with a high degree of
accuracy.
Sources of noise
Noise is unwanted changes to the MR signal from elements
not of interest to the study. The five main sources of noise in fMRI are
thermal noise, system noise, physiological noise, random neural
activity and differences in both mental strategies and behavior across
people and across tasks within a person. Thermal noise multiplies in
line with the static field strength, but physiological noise multiplies
as the square of the field strength. Since the signal also multiplies as
the square of the field strength, and since physiological noise is a
large proportion of total noise, higher field strengths above 3T do not always produce proportionately better images.
Heat causes electrons to move around and distort the
current in the fMRI detector, producing thermal noise. Thermal noise
rises with the temperature. It also depends on the range of frequencies
detected by the receiver coil and its electrical resistance. It affects
all voxels similarly, independent of anatomy.
System noise is from the imaging hardware. One form is
scanner drift, caused by the superconducting magnet's field drifting
over time. Another form is changes in the current or voltage
distribution of the brain itself inducing changes in the receiver coil
and reducing its sensitivity. A procedure called impedance matching is
used to bypass this inductance effect. There could also be noise from
the magnetic field not being uniform. This is often adjusted for by
using shimming coils, small magnets physically inserted, say into the
subject's mouth, to patch the magnetic field. The nonuniformities are
often near brain sinuses such as the ear and plugging the cavity for
long periods can be discomfiting. The scanning process acquires the MR
signal in k-space, in which overlapping spatial frequencies (that is
repeated edges in the sample's volume) are each represented with lines.
Transforming this into voxels introduces some loss and distortions.
Physiological noise is from head and brain movement in the
scanner from breathing, heart beats, or the subject fidgeting, tensing,
or making physical responses such as button presses. Head movements
cause the voxel-to-neurons mapping to change while scanning is in
progress. Noise due to head movement is a particular issue when working
with children, although there are measures that can be taken to reduce
head motion when scanning children, such as changes in experimental
design and training prior to the scanning session. Since fMRI is acquired in slices, after movement, a voxel continues to
refer to the same absolute location in space while the neurons
underneath it would have changed. Another source of physiological noise
is the change in the rate of blood flow, blood volume, and use of oxygen
over time. This last component contributes to two-thirds of
physiological noise, which, in turn, is the main contributor to total
noise.
Even with the best experimental design, it is not possible
to control and constrain all other background stimuli impinging on a
subject—scanner noise, random thoughts, physical sensations, and the
like. These produce neural activity independent of the experimental
manipulation. These are not amenable to mathematical modeling and have
to be controlled by the study design.
A person's strategies to respond or react to a stimulus,
and to solve problems, often change over time and over tasks. This
generates variations in neural activity from trial to trial within a
subject. Across people too neural activity differs for similar reasons.
Researchers often conduct pilot studies to see how participants
typically perform for the task under consideration. They also often
train subjects how to respond or react in a trial training session prior
to the scanning one.
Preprocessing
The scanner platform generates a 3D
volume of the subject's head every TR. This consists of an array of
voxel intensity values, one value per voxel in the scan. The voxels are
arranged one after the other, unfolding the three-dimensional structure
into a single line. Several such volumes from a session are joined to
form a 4D volume corresponding to a run, for the time period the subject stayed in the scanner without adjusting head position. This 4D volume is the starting point for analysis. The first part of that analysis is preprocessing.
The first step in preprocessing is conventionally slice
timing correction. The MR scanner acquires different slices within a
single brain volume at different times, and hence the slices represent
brain activity at different timepoints. Since this complicates later
analysis, a timing correction is applied to bring all slices to the same
timepoint reference. This is done by assuming the timecourse of a voxel
is smooth when plotted as a dotted line. Hence the voxel's intensity
value at other times not in the sampled frames can be calculated by
filling in the dots to create a continuous curve.
Head motion correction is another common preprocessing
step. When the head moves, the neurons under a voxel move and hence its
timecourse now represents largely that of some other voxel in the past.
Hence the timecourse curve is effectively cut and pasted from one voxel
to another. Motion correction tries different ways of undoing this to
see which undoing of the cut-and-paste produces the smoothest timecourse
for all voxels. The undoing is by applying a rigid-body transform to
the volume, by shifting and rotating the whole volume data to account
for motion. The transformed volume is compared statistically to the
volume at the first timepoint to see how well they match, using a cost
function such as correlation or mutual information.
The transformation that gives the minimal cost function is chosen as
the model for head motion. Since the head can move in a vastly varied
number of ways, it is not possible to search for all possible
candidates; nor is there right now an algorithm that provides a globally
optimal solution independent of the first transformations we try in a
chain.
Distortion corrections account for field nonuniformities
of the scanner. One method, as described before, is to use shimming
coils. Another is to recreate a field map of the main field by acquiring
two images with differing echo times. If the field were uniform, the
differences between the two images also would be uniform. Note these are
not true preprocessing techniques since they are independent of the
study itself. Bias field estimation is a real preprocessing technique
using mathematical models of the noise from distortion, such as Markov random fields and expectation maximization algorithms, to correct for distortion.
In general, fMRI studies acquire both many functional
images with fMRI and a structural image with MRI. The structural image
is usually of a higher resolution and depends on a different signal, the
T1 magnetic field decay after excitation. To demarcate regions of
interest in the functional image, one needs to align it with the
structural one. Even when whole-brain analysis is done, to interpret the
final results, that is to figure out which regions the active voxels
fall in, one has to align the functional image to the structural one.
This is done with a coregistration algorithm that works similar to the
motion-correction one, except that here the resolutions are different,
and the intensity values cannot be directly compared since the
generating signal is different.
Typical MRI studies scan a few different subjects. To
integrate the results across subjects, one possibility is to use a
common brain atlas, and adjust all the brains to align to the atlas, and
then analyze them as a single group. The atlases commonly used are the
Talairach one, a single brain of an elderly woman created by Jean Talairach, and the Montreal Neurological Institute
(MNI) one. The second is a probabilistic map created by combining scans
from over a hundred individuals. This normalization to a standard
template is done by mathematically checking which combination of
stretching, squeezing, and warping reduces the differences between the
target and the reference. While this is conceptually similar to motion
correction, the changes required are more complex than just translation
and rotation, and hence optimization even more likely to depend on the
first transformations in the chain that is checked.
Temporal filtering is the removal of frequencies of no
interest from the signal. A voxel's intensity change over time can be
represented as the sum of a number of different repeating waves with
differing periods and heights. A plot with these periods on the x-axis
and the heights on the y-axis is called a power spectrum, and this plot is created with the Fourier transform
technique. Temporal filtering amounts to removing the periodic waves
not of interest to us from the power spectrum, and then summing the
waves back again, using the inverse Fourier transform
to create a new timecourse for the voxel. A high-pass filter removes
the lower frequencies, and the lowest frequency that can be identified
with this technique is the reciprocal of twice the TR. A low-pass filter
removes the higher frequencies, while a band-pass filter removes all
frequencies except the particular range of interest.
Smoothing, or spatial filtering, is the idea of averaging
the intensities of nearby voxels to produce a smooth spatial map of
intensity change across the brain or region of interest. The averaging
is often done by convolution with a Gaussian filter,
which, at every spatial point, weights neighboring voxels by their
distance, with the weights falling exponentially following the bell curve.
If the true spatial extent of activation, that is the spread of the
cluster of voxels simultaneously active, matches the width of the filter
used, this process improves the signal-to-noise ratio.
It also makes the total noise for each voxel follow a bell-curve
distribution, since adding together a large number of independent,
identical distributions of any kind produces the bell curve as the limit
case. But if the presumed spatial extent of activation does not match
the filter, signal is reduced.
Statistical analysis
These
fMRI images are from a study showing parts of the brain lighting up on
seeing houses and other parts on seeing faces. The 'r' values are
correlations, with higher positive or negative values indicating a
stronger relationship (i.e., a better match).
One common approach to analysing fMRI data is to consider each voxel separately within the framework of the general linear model. The model assumes, at every time point, that the hemodynamic response
(HR) is equal to the scaled and summed version of the events active at
that point. A researcher creates a design matrix specifying which events
are active at any timepoint. One common way is to create a matrix with
one column per overlapping event, and one row per time point, and to
mark it if a particular event, say a stimulus, is active at that time
point. One then assumes a specific shape for the HR, leaving only its
amplitude changeable in active voxels. The design matrix and this shape
are used to generate a prediction of the exact HR of the voxel at every
timepoint, using the mathematical procedure of convolution. This prediction does not include the scaling required for every event before summing them.
The basic model assumes the observed HR is the predicted
HR scaled by the weights for each event and then added, with noise mixed
in. This generates a set of linear equations with more equations than
unknowns. A linear equation has an exact solution, under most
conditions, when equations and unknowns match. Hence one could choose
any subset of the equations, with the number equal to the number of
variables, and solve them. But, when these solutions are plugged into
the left-out equations, there will be a mismatch between the right and
left sides, the error. The GLM model attempts to find the scaling
weights that minimize the sum of the squares of the error. This method
is provably optimal if the error were distributed as a bell curve, and
if the scaling-and-summing model were accurate. For a more mathematical description of the GLM model, see generalized linear models.
The GLM model does not take into account the contribution
of relationships between multiple voxels. Whereas GLM analysis methods
assess whether a voxel or region's signal amplitude is higher or lower
for one condition than another, newer statistical models such as
multi-voxel pattern analysis (MVPA), utilize the unique contributions of
multiple voxels within a voxel-population. In a typical implementation,
a classifier or more basic algorithm is trained to distinguish trials
for different conditions within a subset of the data. The trained model
is then tested by predicting the conditions of the remaining
(independent) data. This approach is most typically achieved by training
and testing on different scanner sessions or runs. If the classifier is
linear, then the training model is a set of weights used to scale the
value in each voxel before summing them to generate a single number that
determines the condition for each testing set trial. More information
on training and testing classifiers is at statistical classification. MVPA allows for inferences about the information content of the underlying neural representations reflected in the BOLD signal, though there is a controversy about whether information detected by
this method reflects information encoded at the level of columns, or
higher spatial scales. Moreover, its harder to decode information from the prefrontal cortex
compared to visual cortex and such differences in sensitivity across
regions makes comparisons across regions problematic. Another method used the same fMRI dataset for visual object
recognition in the human brain is depending on multi-voxel pattern
analysis (fMRI voxels) and multi-view learning which is described in, this method used meta-heuristic search and mutual information to
eliminate noisy voxels and select the significant BOLD signals.
Combining with other methods
It is common to combine fMRI signal acquisition with
tracking of participants' responses and reaction times. Physiological
measures such as heart rate, breathing, skin conductance (rate of
sweating), and eye movements are sometimes captured simultaneously with
fMRI. The method can also be combined with other brain-imaging
techniques such as transcranial stimulation, direct cortical stimulation and, especially, EEG. The fMRI procedure can also be combined with near-infrared spectroscopy (NIRS) to have supplementary information about both oxyhemoglobin and deoxyhemoglobin.
The fMRI technique can complement or supplement other
techniques because of its unique strengths and gaps. It can
noninvasively record brain signals without risks of ionising radiation
inherent in other scanning methods, such as CT or PET scans. It can also record signal from all regions of the brain, unlike EEG/MEG, which are biased toward the cortical surface. But fMRI temporal resolution is poorer than that of EEG since the HR
takes tens of seconds to climb to its peak. Combining EEG with fMRI is
hence potentially powerful because the two have complementary
strengths—EEG has high temporal resolution, and fMRI high spatial
resolution. But simultaneous acquisition needs to account for the EEG
signal from varying blood flow triggered by the fMRI gradient field, and
the EEG signal from the static field. For details, see EEG vs fMRI.
While fMRI stands out due to its potential to capture
neural processes associated with health and disease, brain stimulation
techniques such as transcranial magnetic stimulation (TMS) have the
power to alter these neural processes. Therefore, a combination of both
is needed to investigate the mechanisms of action of TMS treatment and
on the other hand introduce causality into otherwise pure correlational
observations. The current state-of-the-art setup for these concurrent
TMS/fMRI experiments comprises a large-volume head coil, usually a
birdcage coil, with the MR-compatible TMS coil being mounted inside that
birdcage coil. It was applied in a multitude of experiments studying
local and network interactions. However, classic setups with the TMS
coil placed inside MR birdcage-type head coil are characterised by poor
signal to noise ratios compared to multi-channel receive arrays used in
clinical neuroimaging today. Moreover, the presence of the TMS coil
inside the MR birdcage coil causes artefacts beneath the TMS coil, i.e.
at the stimulation target. For these reasons new MR coil arrays were
currently developed dedicated to concurrent TMS/fMRI experiments.
Issues in fMRI
Design
If the baseline condition is too close to maximum activation, certain processes may not be represented appropriately. Another limitation on experimental design is head motion, which can lead to artificial intensity changes of the fMRI signal.
Block versus event-related design
In a block design, two or more conditions are alternated
by blocks. Each block will have a duration of a certain number of fMRI
scans and within each block only one condition is presented. By making
the conditions differ in only the cognitive process of interest, the
fMRI signal that differentiates the conditions should represent this
cognitive process of interest. This is known as the subtraction
paradigm. The increase in fMRI signal in response to a stimulus is additive. This means that the amplitude of the hemodynamic response
(HR) increases when multiple stimuli are presented in rapid succession.
When each block is alternated with a rest condition in which the HR has
enough time to return to baseline, a maximum amount of variability is
introduced in the signal. As such, we conclude that block designs offer
considerable statistical power. There are however severe drawbacks to this method, as the signal is
very sensitive to signal drift, such as head motion, especially when
only a few blocks are used. Another limiting factor is a poor choice of
baseline, as it may prevent meaningful conclusions from being drawn.
There are also problems with many tasks lacking the ability to be
repeated. Since within each block only one condition is presented, randomization
of stimulus types is not possible within a block. This makes the type
of stimulus within each block very predictable. As a consequence,
participants may become aware of the order of the events.
Event-related
designs allow more real world testing, however, the statistical power
of event related designs is inherently low, because the signal change in
the BOLD fMRI signal following a single stimulus presentation is small.
Both block and event-related designs are based on the subtraction paradigm,
which assumes that specific cognitive processes can be added
selectively in different conditions. Any difference in blood flow (the
BOLD signal) between these two conditions is then assumed to reflect the
differing cognitive process. In addition, this model assumes that a
cognitive process can be selectively added to a set of active cognitive
processes without affecting them.
Overlap of signals
Overlapping signals in fMRI are a significant challenge in
cognitive neuroscience research, particularly when multiple stimuli or
tasks are presented in close temporal proximity. The BOLD response has a slow temporal resolution compared to the rapid
succession of cognitive events. This causes signals from different brain
processes to overlap, making it difficult to differentiate which neural
activity is associated with specific stimuli or tasks. This overlap
reduces the precision of event-related fMRI analyses, complicating
interpretations of brain function.
Traditional fMRI designs, such as block or event-related
designs, face limitations in managing this signal overlap, especially in
studies with non-randomized, alternating designs, where the same tasks
or stimuli may appear repeatedly and close together. As a result, the timing of stimuli becomes a crucial factor in ensuring
that the fMRI signal from one event is sufficiently distinct from the
next.
To address these challenges, researchers employ techniques
like deconvolution, which mathematically separates the overlapping BOLD
responses. In 2023, Das and colleagues demonstrated various ways on optimizing
timing between individual events such that convolution of signals evoked
by them that are close to each other can be minimized. These methods attempt to estimate the contribution of each neural event
to the overall signal, allowing for more accurate interpretation of
brain activity. Advances in analytical methods, such as specialized
tools for optimizing experimental designs, are crucial for mitigating
the effects of signal overlap and improving the reliability of fMRI
studies.
Baseline versus activity conditions
The brain is never completely at rest. It never stops
functioning and firing neuronal signals, as well as using oxygen as long
as the person in question is alive. In fact, in Stark and Squire's,
2001 study When zero is not zero: The problem of ambiguous baseline conditions in fMRI,
activity in the medial temporal lobe (as well as in other brain
regions) was substantially higher during rest than during several
alternative baseline conditions. The effect of this elevated activity
during rest was to reduce, eliminate, or even reverse the sign of the
activity during task conditions relevant to memory functions. These
results demonstrate that periods of rest are associated with significant
cognitive activity and are therefore not an optimal baseline for
cognition tasks. In order to discern baseline and activation conditions
it is necessary to interpret a lot of information. This includes
situations as simple as breathing. Periodic blocks may result in
identical data of other variance in the data if the person breathes at a
regular rate of 1 breath/5sec, and the blocks occur every 10s, thus
impairing the data.
Reverse inference
Neuroimaging methods such as fMRI and MRI offer a measure
of the activation of certain brain areas in response to cognitive tasks
engaged in during the scanning process. Data obtained during this time
allow cognitive neuroscientists to gain information regarding the role
of particular brain regions in cognitive function. However, an issue arises when certain brain regions are alleged by
researchers to identify the activation of previously labeled cognitive
processes. Poldrack clearly describes this issue:
The usual kind of inference that is drawn
from neuroimaging data is of the form 'if cognitive process X is
engaged, then brain area Z is active.' Perusal of the discussion
sections of a few fMRI articles will quickly reveal, however, an
epidemic of reasoning taking the following form:
(1) In the present study, when task comparison A was presented, brain area Z was active.
(2) In other studies, when cognitive process X was putatively engaged, then brain area Z was active.
(3) Thus, the activity of area Z in the present study demonstrates engagement of cognitive process X by task comparison A.
This is a 'reverse inference', in that it reasons
backwards from the presence of brain activation to the engagement of a
particular cognitive function.
Reverse inference demonstrates the logical fallacy
of affirming what you just found, although this logic could be
supported by instances where a certain outcome is generated solely by a
specific occurrence. With regard to the brain and brain function it is
seldom that a particular brain region is activated solely by one
cognitive process. Some suggestions to improve the legitimacy of reverse inference have
included both increasing the selectivity of response in the brain region
of interest and increasing the prior probability of the cognitive process in question. However, Poldrack suggests that reverse inference should be used merely as a guide to
direct further inquiry rather than a direct means to interpret results.
Forward inference
Forward inference is a data driven method that uses
patterns of brain activation to distinguish between competing cognitive
theories. It shares characteristics with cognitive psychology's
dissociation logic and philosophy's forward chaining. For example, Henson discusses forward inference's contribution to the "single process theory vs. dual process theory" debate with regard to recognition memory.
Forward inference supports the dual process theory by demonstrating
that there are two qualitatively different brain activation patterns
when distinguishing between "remember vs. know judgments". The main issue with forward inference is that it is a correlational method.
Therefore, one cannot be completely confident that brain regions
activated during cognitive process are completely necessary for that
execution of those processes. In fact, there are many known cases that demonstrate just that. For
example, the hippocampus has been shown to be activated during classical conditioning, however lesion studies have demonstrated that classical conditioning can occur without the hippocampus.
Health risks
The most common risk to participants in an fMRI study is claustrophobia and there are reported risks for pregnant women to go through the scanning process. Scanning sessions also subject participants to loud high-pitched noises from Lorentz forces
induced in the gradient coils by the rapidly switching current in the
powerful static field. The gradient switching can also induce currents
in the body causing nerve tingling. Implanted medical devices such as pacemakers
could malfunction because of these currents. The radio-frequency field
of the excitation coil may heat up the body, and this has to be
monitored more carefully in those running a fever, the diabetic, and
those with circulatory problems. Local burning from metal necklaces and
other jewellery is also a risk.
The strong static magnetic field can cause damage by pulling in nearby heavy metal objects converting them to projectiles.
There is no proven risk of biological harm from even very powerful static magnetic fields.However, genotoxic (i.e., potentially carcinogenic) effects of MRI scanning have been demonstrated in vivo and in vitro, leading a recent review to recommend "a need for further studies and
prudent use in order to avoid unnecessary examinations, according to the
precautionary principle". In a comparison of genotoxic effects of MRI compared with those of CT
scans, Knuuti et al. reported that even though the DNA damage detected
after MRI was at a level comparable to that produced by scans using
ionizing radiation (low-dose coronary CT angiography, nuclear imaging,
and X-ray angiography), differences in the mechanism by which this
damage takes place suggests that the cancer risk of MRI, if any, is
unknown.
Advanced methods
The first fMRI studies validated the technique against
brain activity known, from other techniques, to be correlated to tasks.
By the early 2000s, fMRI studies began to discover novel correlations.
Still their technical disadvantages have spurred researchers to try more
advanced ways to increase the power of both clinical and research
studies.
Better spatial resolution
MRI, in general, has better spatial resolution than EEG
and MEG, but not as good a resolution as invasive procedures such as
single-unit electrodes. While typical resolutions are in the millimeter
range, ultra-high-resolution MRI or MR spectroscopy works at a
resolution of tens of micrometers. It uses 7T
fields, small-bore scanners that can fit small animals such as rats,
and external contrast agents such as fine iron oxide. Fitting a human
requires larger-bore scanners, which make higher fields strengths harder
to achieve, especially if the field has to be uniform; it also requires
either internal contrast such as BOLD or a non-toxic external contrast
agent unlike iron oxide.
Parallel imaging is another technique to improve spatial
resolution. This uses multiple coils for excitation and reception.
Spatial resolution improves as the square root of the number of coils
used. This can be done either with a phased array where the coils are
combined in parallel and often sample overlapping areas with gaps in the
sampling or with massive coil arrays, which are a much denser set of
receivers separate from the excitation coils. These, however, pick up
signals better from the brain surface, and less well from deeper
structures such as the hippocampus.
Better temporal resolution
Temporal resolution of fMRI is limited by: (1) the
feedback mechanism that raises the blood flow operating slowly; (2)
having to wait until net magnetization recovers before sampling a slice
again; and (3) having to acquire multiple slices to cover the whole
brain or region of interest. Advanced techniques to improve temporal
resolution address these issues. Using multiple coils speeds up
acquisition time in exact proportion to the coils used. Another
technique is to decide which parts of the signal matter less and drop
those. This could be either those sections of the image that repeat
often in a spatial map (that is small clusters dotting the image
periodically) or those sections repeating infrequently (larger
clusters). The first, a high-pass filter in k-space, has been proposed
by Gary H. Glover and colleagues at Stanford. These mechanisms assume the researcher has an idea of the expected shape of the activation image.
Typical gradient-echo EPI uses two gradient coils within a
slice, and turns on first one coil and then the other, tracing a set of
lines in k-space. Turning on both gradient coils can generate angled
lines, which cover the same grid space faster. Both gradient coils can
also be turned on in a specific sequence to trace a spiral shape in
k-space. This spiral imaging sequence acquires images faster than
gradient-echo sequences, but needs more math transformations (and
consequent assumptions) since converting back to voxel space requires
the data be in grid form (a set of equally spaced points in both
horizontal and vertical directions).
New contrast mechanisms
BOLD contrast depends on blood flow, which is both
sluggish in response to stimulus and subject to noisy influences. Other
biomarkers now looked at to provide better contrast include temperature,
acidity/alkalinity (pH), calcium-sensitive agents, neuronal magnetic
field, and the Lorentz effect. Temperature contrast depends on changes
in brain temperature from its activity. The initial burning of glucose
raises the temperature, and the subsequent inflow of fresh, cold blood
lowers it. These changes alter the magnetic properties of tissue. Since the
internal contrast is too difficult to measure, external agents such as thulium
compounds are used to enhance the effect. Contrast based on pH depends
on changes in the acid/alkaline balance of brain cells when they go
active. This too often uses an external agent. Calcium-sensitive agents
make MRI more sensitive to calcium concentrations, with calcium ions
often being the messengers for cellular signalling
pathways in active neurons. Neuronal magnetic field contrast measures
the magnetic and electric changes from neuronal firing directly.
Lorentz-effect imaging tries to measure the physical displacement of
active neurons carrying an electric current within the strong static
field.
Vascular-space occupancy (VASO) measures changes in
cerebral blood volume (CBV), which is a less ambiguous marker of neural
activity than BOLD. The signal intensity is inversely proportional to
CBV, with typical signal changes around -1.5%. It relies on the T₁
difference between blood and the surrounding tissue. Because it nulls the blood signal, VASO eliminates the large veins
contribution to the MR signal, reducing the risk of falsely localizing
brain activity to nearby draining veins, and achieving high spatial
specificity, which makes the technique well-suited for layer-specific
fMRI. VASO allows for the reliable quantification of physiological
parameters such as the cerebral metabolic rate of oxygen or the oxygen
extraction fraction. However, the approach suffers from some limitations
compared to techniques that rely on the BOLD signal. VASO generally has
lower sensitivity, lower SNR and lower CNR, as well as reduced imaging
efficiency and higher susceptibility to motion artifacts.
Emerging acquisition strategies
Balanced Steady-State Free Precession (bSSFP) fMRI: bSSFP
offers high SNR efficiency due to optimized magnetization usage, leading
to high tSNR. It also features reduced distortion because of its short
readouts, minimizing susceptibility-induced distortion and signal
dropout compared to GRE-EPI. It's compatible with advanced short-readout
strategies like multi-shot, spiral, and radial trajectories. However,
bSSFP is prone to banding artifacts from off-resonance effects and has a
complex contrast mechanism influenced by T₂/T₁, diffusion, and
off-resonance. It also tends to have a higher SAR compared to GRE
sequences. Pass-Band bSSFP: This variant has a flat magnitude response over a broad
frequency range (approximately 75% of 1/TR). Its contrast is T₂-based,
driven by diffusion in susceptibility gradients and intravascular T₂
changes, with low sensitivity to frequency shifts. While offering lower
contrast than long-TE GRE, it provides superior robustness to
off-resonance and shim variations, making efficient whole-brain coverage
achievable with minimal phase cycling. Larger Flip angles could be used
to operate in Passband region.
Transition-Band bSSFP: This type operates within a narrow frequency
range (around 10–15% of 1/TR) with a steep magnitude and phase response.
Its BOLD sensitivity is frequency-driven, where small frequency shifts
cause large signal changes, potentially yielding high functional
contrast (10–40%). However, this makes it highly dependent on the
baseline resonance frequency of the voxel and critical of shimming
quality, challenging whole-brain coverage without multiple acquisitions.
Applications: bSSFP techniques can be beneficial for layer-specific
imaging or studies requiring high SNR and excellent temporal resolution,
especially in low-field or low-SNR brain regions.
Multi-echo, Multi-contrast (SAGE) fMRI: SAGE techniques aim to combine
the high sensitivity of multi-gradient echo (MGE) with the microvascular
specificity of spin-echo (SE). They simultaneously acquire multiple
gradient-echoes and spin-echoes (e.g., 2 GRE, 2 Asymmetric SE, 1 SE),
allowing for optimized Contrast-to-Noise Ratio (CNR) and temporal
Signal-to-Noise Ratio (tSNR) across varying T₂* values. Echoes can be
weighted to emphasize either T₂*-BOLD contrast (wT₂*), similar to GRE,
or T₂-BOLD contrast (wT₂), similar to SE. For instance, wT₂* weighting
uses pre-refocusing GREs and Asymmetric Spin Echoes (ASEs), while wT₂
weighting uses post-refocusing ASEs and the pure SE. This method is
particularly useful for studies focused on vascular physiology or when
needing to avoid interscan variability. A limitation is its reliance on
acceleration methods like multiband (MB) or SENSE.
Silent fMRI: Based on 3D radial Rotating Ultra-Fast Imaging Sequence
(RUFIS), Looping Star enhances RUFIS/Zero Echo Time (ZTE) by adding
time-multiplexed gradient refocusing. This enables multi-echo
acquisition beyond the initial FID (TE ≈ 0) to provide essential T₂*
BOLD contrast. Its primary advantage is significantly reduced acoustic
noise, approximately 25 dBA quieter than conventional GRE-EPI (a ~98%
reduction). Limitations include potential echo mixing artifacts
requiring correction and possibly reduced tSNR and BOLD sensitivity
compared to GRE-EPI. Studies have shown that while looping star
identifies similar activation networks to GE-EPI, it may exhibit reduced
activation extent and lower T-values due to lower tSNR. Its BOLD sensitivity was found to be around 69.5% relative to GE-EPI.
Applications: Silent fMRI techniques are particularly valuable for
auditory tasks (to avoid stimulus-correlated scanner noise), pediatric
studies, or examining subjects sensitive to loud noise.
Commercial use
Some experiments have shown the neural correlates of peoples' brand preferences. Samuel M. McClure used fMRI to show the dorsolateral prefrontal cortex, hippocampus and midbrain were more active when people knowingly drank Coca-Cola as opposed to when they drank unlabeled Coke. Other studies have shown the brain activity that characterizes men's
preference for sports cars, and even differences between Democrats and
Republicans in their reaction to campaign commercials with images of the
9/11 attacks. Neuromarketing
companies have seized on these studies as a better tool to poll user
preferences than the conventional survey technique. One such company was
BrightHouse, now shut down. Another is Oxford, UK-based Neurosense, which advises clients how they could potentially use fMRI as part of their marketing business activity. A third is Sales Brain in California.
At least two companies have been set up to use fMRI in lie detection: No Lie MRI and the Cephos Corporation. No Lie MRI charges close to $5000 for its services. These companies
depend on evidence such as that from a study by Joshua Greene at Harvard University suggesting the prefrontal cortex is more active in those contemplating lying.
However, there is still a fair amount of controversy over
whether these techniques are reliable enough to be used in a legal
setting. Some studies indicate that while there is an overall positive
correlation, there is a great deal of variation between findings and in
some cases considerable difficulty in replicating the findings. A federal magistrate judge in Tennessee prohibited fMRI evidence to
back up a defendant's claim of telling the truth, on the grounds that
such scans do not measure up to the legal standard of scientific
evidence. Most researchers agree that the ability of fMRI to detect deception in a real life setting has not been established.
Use of the fMRI has been left out of legal debates
throughout its history. Use of this technology has not been allowed due
to holes in the evidence supporting fMRI. First, most evidence
supporting fMRIs accuracy was done in a lab under controlled
circumstances with solid facts. This type of testing does not pertain to
real life. Real-life scenarios can be much more complicated with many
other affecting factors. It has been shown that many other factors affect BOLD other than a
typical lie. There have been tests done showing that drug use alters
blood flow in the brain, which drastically affects the outcome of BOLD
testing. Furthermore, individuals with diseases or disorders such as
schizophrenia or compulsive lying can lead to abnormal results as well.
Lastly, there is an ethical question relating to fMRI scanning. This
testing of BOLD has led to controversy over if fMRIs are an invasion of
privacy. Being able to scan and interpret what people are thinking may
be thought of as immoral and the controversy still continues.
Because of these factors and more, fMRI evidence has been
excluded from any form of legal system. The testing is too uncontrolled
and unpredictable. Therefore, it has been stated that fMRI has much more
testing to do before it can be considered viable in the eyes of the
legal system.
Criticism
Some scholars have criticized fMRI studies for problematic statistical analyses, often based on low-power, small-sample studies. Other fMRI researchers have defended their work as valid. In 2018, Turner and colleagues have suggested that the small sizes
affect the replicability of task-based fMRI studies and claimed that
even datasets with at least 100 participants the results may not be well
replicated, although there are debates on it.
In one real but satirical fMRI study, a dead salmon
was shown pictures of humans in different emotional states. The authors
provided evidence, according to two different commonly used statistical
tests, of areas in the salmon's brain suggesting meaningful activity.
The study was used to highlight the need for more careful statistical
analyses in fMRI research, given the large number of voxels in a typical
fMRI scan and the multiple comparisons problem. Before the controversies were publicized in 2010, between 25 and 40%
of studies on fMRI being published were not using the corrected
comparisons. But by 2012, that number had dropped to 10%. Dr. Sally Satel, writing in Time, cautioned that while brain scans have
scientific value, individual brain areas often serve multiple purposes
and "reverse inferences" as commonly used in press reports carry a
significant chance of drawing invalid conclusions. In 2015, it was discovered that a statistical bug was found in the fMRI
computations which likely invalidated at least 40,000 fMRI studies
preceding 2015, and researchers suggest that results prior to the bug
fix cannot be relied upon. Furthermore, it was later shown that how one sets the parameters in
the software determines the false positive rate. In other words, study
outcome can be determined by changing software parameters.
In 2020 professor Ahmad Hariri, (Duke University) one of
the first researchers to use fMRI, performed a largescale experiment
that sought to test the reliability of fMRI on individual people.
In the study, he copied protocols from 56 published papers in psychology
that used fMRI. The results suggest that fMRI has poor reliability when
it comes to individual cases, but good reliability when it comes to
general human thought patterns.
A 2025 study published in Nature Neuroscience found that BOLD signals in fMRI did not consistently relate to changes in neuronal activity.
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