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Is intelligence correlated with neural calorie usage?

Is intelligence correlated with neural calorie usage?


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The human brain uses a lot of calories in comparison to other species, that is usually explained by referencing the intelligence disparity between Humans and other Animals. My question is how far does that go? Do individuals with radically improved intelligence also use radically more calories? And peripherally, does that caloric need place an upper limit on human intelligence?


Superfluidity: Fluid Intelligence Goes Beyond Brain Size

We all know the feeling of having an “Aha!” moment—when your thoughts and ideas seem to flow without friction or viscosity and come together in a millisecond of perfect harmony. Having an epiphany is directly linked to fluid intelligence, which is the ability to connect seemingly unrelated ideas in new and useful ways.

Superfluidity is a term I borrowed from the world of physics to describe the highest form of what Mihaly Csikszentmihályi describes as flow. I also use this term to describe the optimization of fluid intelligence that occurs during Eureka! moments. I've learned from personal experience that superfluidity is an episodic experience in which your mind, body, and brain seem to function in a state of "super flow" marked by zero friction, viscosity, or entropy between your thoughts, emotions, and movements.

What Is Fluid Intelligence?

Fluid intelligence is the ability to think creatively, adapt to new situations, and solve problems you've often never encountered before in novel situations. Fluid intelligence generally involves the ability to use critical thinking—along with explicit and implicit knowledge—to identify patterns and connect-the-dots in a personal and original way. As Albert Szent-Györgyi once said, "Thus, the task is not to see what nobody has seen, but to think what nobody has thought, about what everybody sees."

“Aha!” moments—in which you wrestle to find an analytic solution and have an intuitive flash of insight that provides a correct answer—exemplify fluid intelligence. On the other hand, crystallized intelligence is represented by the strict analytic ability to utilize skills, knowledge, and experience to answer declarative questions solely using explicit knowledge.

Crystallized intelligence is a form of declarative memory that represents cerebral facts that you could memorize and respond to in a basic multiple choice standardized test. As the name implies, crystallized intelligence is not fluid or malleable. In many ways, the emphasis on gauging intelligence based solely on academic tests that only measure crystallized intelligence (under initiatives such as No Child Left Behind) has caused the importance of fluid intelligence to be grossly undervalued and allowed to atrophy in a digital era.

Arthur Koestler beautifully described the feeling of having an "Aha!" moment in a way that touches on the difference between fluid intelligence and crystallized intelligence. Koestler said,

“The moment of truth, the sudden emergence of a new insight, is an act of intuition. Such intuitions give the appearance of miraculous flushes, or short-circuits of reasoning. In fact they may be likened to an immersed chain, of which only the beginning and the end are visible above the surface consciousness. The diver vanishes at one end of the chain and comes up at the other end, guided by invisible links.”

For a long time, cognitive neuroscientists have been conducting research to better understand the biological foundations of various types of human intelligence. Decades of research have revealed that general intelligence is correlated with two brain-based biomarkers: the concentration of the brain biochemical N-acetyl aspartate (NAA), which can be measured by proton magnetic resonance spectroscopy (MRS) and total brain volume, which can be measured using structural MRI brain imaging.

Fluid Intelligence vs. Crystallized Intelligence

In March 2016, I reported on a study from the Beckman Institute at the University of Illinois in Urbana-Champaign which found that higher concentrations of NAA in motor regions of the brain were specifically linked to higher levels of fluid intelligence.

NAA is a biochemical marker used to gauge the neural energy production within a specific brain area. In a statement, lead author Aki Nikolaidis said, “The brain’s motor regions have a role in planning and visualizing movements as well as carrying them out. Mental visualization is a key element of fluid intelligence.”

The researchers concluded that while overall brain size can be genetically determined and not readily changed, NAA levels and brain metabolism may respond to health interventions including diet, exercise, or cognitive training—all of which can improve fluid intelligence.

This week, a new study by researchers at the Beckman Institute offers more clarity on how brain structure and chemistry give rise to specific aspects of fluid intelligence. The June 2016 study, "Dissociable Brain Biomarkers of Fluid Intelligence,” appears in the journal NeuroImage.

What makes this study groundbreaking is that the researchers have linked higher concentrations of NAA energy production in the brain with an improved ability to specifically solve verbal and spatial problems. However, the researchers found an association between brain size and number-related problem-solving.

In a statement, University of Illinois postdoctoral researcher Erick Paul, who led the work with research scientist Ryan Larsen and Illinois neuroscience professor Aron Barbey, said,

"Surely there are many things about the brain that determine a person's intelligence, and the goal is to try to tease apart that puzzle. These two brain biomarkers, brain volume and NAA, are each giving us independent information about fluid intelligence. There are different properties of the brain that we can measure, and these different properties go with these different facets of fluid intelligence.

In our data, we observed two facets of fluid intelligence—one that involves quantitative or numeric reasoning, and another that involves verbal or spatial reasoning. A similar separation of reasoning abilities has been demonstrated in previous studies.

We found that the quantitative reasoning component of intelligence correlated with brain volume, but not with the concentration of NAA in the brain. And the verbal and spatial components of intelligence correlated with NAA, but not with brain volume.The findings add to the evidence that fluid intelligence involves distinct yet interrelated processes in the brain."

The analysis for this study involved 211 research subjects, making it the largest study to date linking brain chemistry and intelligence in living humans. The researchers emphasize that more studies will be needed to confirm and extend the findings.

"Our findings contribute to a growing body of evidence to suggest that intelligence reflects multiple levels of organization in the brain—spanning neuroanatomy, for example brain size, and neurophysiology, such as brain metabolism—and that specific properties of the brain provide a powerful lens to investigate and understand the nature of specific intellectual abilities," Barbey concluded.

The "Book Smarts" of Crystallized Intelligence Are Often Overrated

I’ve been fascinated with the brain science of various types of intelligence and how we “think about our thinking” for as long as I can remember. For example, in sports, creating fluidity and maintaining the "grace under pressure" necessary not to choke, fumble, or drop the ball generally requires not overthinking. As a science writer, trying to connect the findings of various studies in novel ways to support my original hypotheses requires a combination of crystallized intelligence and fluid intelligence.

My father, Richard M. Bergland, was a world-renowned neurosurgeon, neuroscientist, and author of The Fabric of Mind (Viking). In his youth, he was a nationally ranked tennis player. My dad would say, “Of this I am absolutely positive, becoming a neurosurgeon was a direct consequence of my eye for the ball.”

My father always saw a strong connection between athletics and life success. However, it wasn't until the final years of his life that he hypothesized that the cerebellum (Latin for "little brain") might be playing a big role in many types of intelligence and cognitive abilities. Historically, the cerebellum has been considered to be the seat of muscle memory and coordinating fine-tuned muscle movements.

Growing up, my father expected all three of his children to excel in both sports and academics. My sisters each had a ton of “book smarts." They could retain crystallized intelligence with ease and did very well on tests. My older sister was especially cerebral. She read War and Peace in the fourth grade, got perfect SAT scores, went to Exeter, etc.

On the other hand, I was terrible at school, hated doing homework, and flunked a lot of tests. But, I did have a knack for hitting a tennis ball really well. I was also extremely social and loved keeping my finger on the pulse of America's Top 40 and pop culture. My father thought all the pop music and K-tel records I listened to as a kid were going to turn my cerebrum (Latin for "brain") to mush.

As a consolation for my poor test scores and bad grades, my dad would say, “Your sisters may have the cerebral smarts, but you have athletic genius and superior cerebellar intelligence, Chris.” Cerebellar is the sister word to cerebral and means 'relating to or located in the cerebellum.'

Needless to say, I’ve always had a chip on my shoulder about being perceived as a dumb jock. I'm also hypersensitive to anyone being labeled "smart" just because he or she can retain and regurgitate crystallized knowledge. I identify strongly with the motto of my Alma mater, Hampshire College (a school with no tests or grades) Non Satis Scire, which means, "to know is not enough."

That said, my need to prove to my father that I had both cerebral and cerebellar intelligence was the prime motivating force that drove me to pursue getting a book deal to write The Athlete’s Way: Sweat and the Biology of Bliss (St. Martin’s Press).

Throughout the process of writing the manuscript for my first book in 2005, I relied on my father's lifetime of science training for expert advice and guidance. While writing the book, my dad and I spoke every day, as I picked his brain about neuroscience. Not only did this help strengthen my crystallized intelligence regarding neuroscience, it also fortified our father-son bond.

In my opinion, my father's long-time affiliation with various Ivy League institutions—and his encyclopedic levels of crystallized intelligence—often made him somewhat of an intellectual snob, who spent much of his career primarily interested in "making smart people smarter." Luckily, because of my very rudimentary background in science, my father was forced to slow down and simplify his explanations of complex neuroscience when we worked together. His thought processes benefited from having to simplify things, too.

As I began to reinvent myself and metamorphosize into a more cerebral thinker, my father was pleasantly surprised to see that I had become living proof of the ideas about mindset never being fixed, and the power of neuroplasticity and neurogenesis to reshape the brain—which were cornerstones of The Athlete's Way.

Together, my dad and I created a new split-brain model that put explicit learning in the cerebrum and implicit learning in the cerebellum. When my father died suddenly in 2007, I made a vow to continue his life’s work in neuroscience and to keep my antennae up for clues that help solve the riddles of various types of thinking and human intelligence.

In recent years, I began to expand on the "Bergland Split-Brain Model" to include representations of crystallized and fluid intelligence. In 2009, I came up with the idea that creating "superfluidity of thought" was associated with bridging the gaps between all four brain hemispheres as illustrated in the rudimentary sketch above.

One of the reasons I'm personally excited by the research on fluid intelligence coming out of the Beckman Institute recently is that it plugs into the ideas of optimizing intelligence by a combination of diverse brain structures, unexpected neural connectivity, as well as the importance of NAA chemical drivers.

Conclusions: Unclamping the Prefrontal Cortex Might Improve Fluid Intelligence

Last week, researchers at Stanford University published a study in the journal Cerebral Cortex reporting that enhanced cerebellum connectivity with the cerebrum boosts creative capacity.

It turns out that suppressing the executive-control centers of the cerebrum—and allowing the cerebellum to be the “controller"—increases spontaneous creativity, which is directly linked to fluid intelligence. This is a revolutionary concept that challenges the dubious construct of the “right brain” being our creative epicenter. In many ways, these findings dovetail with the latest research on various facets that drive fluid intelligence.

The latest neuroscience is redefining traditional constructs of how we view intelligence by deconstructing the role that various brain structures, functional connectivity, and brain chemistry play in our daily thinking processes. Stay tuned for updates on these findings!

To read more on fluid intelligence and the cerebellum, check out my previous Psychology Today blog posts,

© 2016 Christopher Bergland. All rights reserved.

Follow me on Twitter @ckbergland for updates on The Athlete’s Way blog posts.

The Athlete's Way ® is a registered trademark of Christopher Bergland.


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Unit 6. Social Intelligence

Social cognition is at the core of human intelligence. Social interactions are the source of much of human knowledge and a large part of everyday human activity. Social behavior also engages a large part of the neural circuitry in the brain and has driven the evolution of the human brain beyond its primate origins. In this unit, you will learn about the neural and perceptual basis of social cognition, and powerful quantitative methods for analyzing fMRI data to probe the neural mechanisms underlying tasks such as inferring the mental states of others.

Nancy Kanwisher touches upon research at the Center for Brains, Minds, and Machines that explores central aspects of social intelligence, such as the recognition of faces, emotions and direction of gaze use of subtle body cues to predict an agent's actions and identification of specialized brain regions for social cognition.

Ken Nakayama first draws parallels between social behavior in humans and animals, and then explores in depth, the ability of humans to recognize faces and to rapidly exploit subtle visual cues to predict the actions of other agents.

In Part 1 of Rebecca Saxe's lecture, you will learn how fMRI studies led to the discovery of a brain region that is selectively engaged in tasks that elicit thoughts about the mental states of others, so-called Theory of Mind. You will also learn about correlation based methods for analyzing fMRI data.

The second part of Rebecca Saxe's lecture shows how recent advances in fMRI analysis methods, such as Multivariate Pattern Analysis (MVPA), may provide a more detailed picture of how the brain encodes high-level cognitive information.


Intelligence: Two Major Schools of Thought

Valerie L. Dammann Northwestern University

I enjoyed reading and rereading Paik's review paper regarding intelligence--one versus multiple. Paik does a good job of covering the two different schools of thought regarding intelligence and what each psychologist believes to be true. I agree with Neisser et al. (1996) that there are different levels of intelligence and each individual has a different measure of intelligence. I also believe that individuals' intellectual performance varies depending on the situation in which they find themselves. This is why I tend to agree more with the psychologists who believe in multiple intelligences, such as Gardner, Sternberg, and Thurstone, rather than those who believe in one intelligence, such as Eysenck, Galton, Jensen, and Spearman.

After researching articles written on these theories, Paik has covered the main points well, although I would like to have seen him go into further detail regarding Gardner's seven different forms of intelligence. Paik does a nice job of explaining Gardner's Premise 2, which takes into account the brain as a major physical determinant of intelligence. Paik also does a nice job of stating the main points of Sternberg and Thurstone, although I would like Paik to have talked a little more about how Thurstone took Spearman's method of factor analysis one step further by rotating the factors (Thurstone, 1938).

Paik concludes his paper by reiterating the two distinct schools of thought regarding intelligence. He briefly covers the main points for one general intelligence as well as for multiple intelligences and concludes with the debate about the nature of intelligence and how no one theory is accepted by all and there is still room for improvement on any given theory. Overall, I enjoyed reading Paik's review paper on intelligence and gained a little more knowledge about the subject.


Future perspectives of forensic epigenomics

Is the unknown trace donor a smoker?

Despite tobacco smoking being widely recognized as having negative health outcomes, a large proportion of the world population still smokes: for example, 19–32% of Europeans [64]. The ability to predict smoking habits from trace DNA would be highly informative in characterizing an unknown trace donor, and thus useful in guiding investigations. Smoking is known to cause DNA damage and telomere shortening [65], and also epigenetic changes, which are caused by effects on DNA methyltransferase expression [66] and DNA methylation patterns [67]. Epigenetic effects of tobacco smoking are also related to cumulative smoke exposure (pack-years) and associated with time since quitting [68,69,70]. The first epigenome-wide association study (EWAS) in blood aimed to identify differential DNA methylation associated with smoking found a single CpG marker (F2RL3) [67]. Following more than 18 additional EWASs in thousands of individuals, various smoking-associated CpGs have been recognized in several genes, including AHRR [71,72,73,74,75,76,77,78,79], ALPP2 [72,73,77,74, 76–78, 80, 81], GFI1 [73, 74, 76, 82], GPR15 [74, 75, 81], and MYO1G [73, 76, 81, 83]. However, the observed per-site DNA methylation differences are relatively small (usually less than 20%) [84]. While most studies have been performed in blood, smoking-associated CpGs have also been identified in other tissues such as lung [72, 79]. While epigenetic effects are persistent for long periods after smoking cessation, some are reversible [68, 77, 85]. One preliminary attempt to predict smoking habits using epigenetics tested a model combining four CpGs for the ability to differentiate between never (n = 120) and former smokers (n = 45), achieving a prediction accuracy of area under the curve (AUC) of 0.83 (AUC values range between 0.5 meaning random prediction and 1.0 meaning completely accurate prediction) [86]. Besides further increasing the prediction accuracy by adding more smoking-predictive CpGs, additional challenges should be considered in the future, such as population-specific effects [76, 87]. One important aspect here is the effect of maternal smoking during pregnancy (for example, 10.7% of pregnant American mothers have been reported to smoke [88]), which could cause similar epigenetic changes in the offspring, lasting into puberty and even adulthood. The influence of passive smoking, which could also impact the epigenome, needs to be considered as well in future practical applications of epigenetics to smoking prediction.

Is the unknown trace donor a drinker?

Alcohol intake highly varies between countries and individuals (more than one-fifth of European adults experience weekly “binge” drinking [89]), and predicting drinking habits can be useful for investigative purposes. Forensic toxicological tests for alcohol metabolite detection exist for blood, urine, and hair, but do not allow inferences regarding regular drinking habits (i.e., how often and how much alcohol is consumed). Due to both genetic [90] and environmental factors [91], differential DNA methylation is evident in regular alcohol consumers versus non-drinkers. A significant increase in global blood methylation has been observed in chronic alcoholics [92], while genes such as the dopamine transporter [93] have been shown to be differentially methylated in alcohol-dependent individuals, although this finding has not yet been replicated in other studies [94]. The first EWAS for alcohol dependency revealed numerous epigenetic markers associated with alcohol metabolism [95], the majority of which (1702 CpGs, p < 0.005) were hypomethylated in alcoholics versus non-drinkers (<17% difference). This finding, however, contradicts alcohol-associated hypermethylated genomes reported elsewhere [92, 96]. In another study, 865 hypomethylated and 716 hypermethylated CpGs were identified [97]. In the largest meta-analysis available, five CpGs were highlighted to explain a substantial proportion (5.2–15%) of interindividual variance in alcohol consumption and were thus proposed as biomarkers for heavy alcohol drinking [98]. A preliminary prediction study achieved AUC > 0.90 based on 144 CpGs [98], a number that from a forensic standpoint is challengingly high due to limited crime scene material and current method capabilities. More candidate markers have been revealed recently, but with effects as small as 1–5% [99]. Alcohol-dependent epigenetic signatures are partly reversible upon abstinence [99] and, as with smoking, prenatal maternal alcohol intake (which occurs in 9.8% of pregnancies worldwide, 2017 [100]) alters gene-specific methylation in placental cord blood [101], and this could potentially lead to false-positive predictions. We expect that future research will identify robust markers to be included in a forensically suitable prediction tool.

Is the unknown trace donor an illicit drug user?

Illicit drug use is prevalent in adults, ranging from 1 to 41% depending on the country [102], and is therefore relevant in characterizing unknown trace donors. Commonly used drugs include cannabis, cocaine, and amphetamines. Depending on the country’s legal framework, thousands of drug-related offences occur annually [102]. Similarly to alcohol, forensic toxicological tests are in widespread use however, they do not provide information on history and habitual use (possibly except for hair analysis). Most studies on drug-induced epigenetic changes have been performed in animal models [103, 104], mainly focusing on chromatin structure and histone modifications [105]. Drug-induced DNA methylation changes have been recently investigated in animal brain regions and neural cells [106]. Global methylation levels were not different in mouse brain and liver following chronic heroin or cocaine treatment [104], but in human brain results were contradictory following methamphetamine dependence [107]. Applying candidate gene approaches, only cannabis and opioid epigenetic effects have been studied in blood thus far. Cannabis-dependent individuals demonstrated altered blood CB1 methylation, which is also detected in cigarette smokers [108]. Almost 200 heroin addicts showed altered blood OPRM1 methylation, but per-site changes were small (<4%) [109] and showed population differences [110]. These so-far small methylation differences indicate that larger numbers of individuals need to be included in association studies however, due to expected difficulties in performing such studies with controlled drug use by study participants, this research question remains in its infancy. Future experiments are also needed to determine whether epigenetic differences are anticipated only in the brain (where the drugs’ effects occur), or whether these are also detectable in forensically more relevant tissues, such as blood. Finally, drug dose-dependent and reversible effects are also expected.

Are there any diet indications for the unknown trace donor?

Predicting an unknown individual’s diet can be of forensic relevance, when special diets are followed (e.g., vegetarian) or special foods are consumed that can potentially be linked with a particular characteristic, such as geographical location, tradition, and religion. Individual staple food comprises various major components such as fruits, vegetables, meat, and fish. Eventually, dietary differences are translated into different intakes of macro- and micronutrients, including carbohydrates, protein, fat, vitamins, and minerals. Nutritional epigenomics is a relatively new, but rapidly growing, research field [111]. Micronutrients including folate and B vitamins seem to play a key role as secondary methyl donors [112]. One of the first epigenome-wide studies demonstrated that prenatal exposure to famine causes lifelong methylation changes [113]. A cross-generational study identified 134 “nutrition-sensitive” regions, implicated with impairments in attention/cognition [114]. Comparing different eating patterns, European vegetarians were found to have approximately 40% decreased MnSOD buccal methylation compared with omnivores [115], while in a smaller-scale study, plasma homocysteine levels showed a significant correlation with global blood DNA methylation in vegetarians [116]. Looking at specific macronutrients, mercury exposure via fish consumption causes SEPP1 hypomethylation [117], and daily intake of roasted meat alters p16 methylation in oesophageal tissue cells [118]. Dietary folate from fortified foods has also been positively associated with LINE-1 blood methylation [119]. Overall, diet involves complex, variable patterns and processes. It is unknown whether inter-relationships between different macro- and micronutrients exist and how unique the observed epigenomic effects are to a specific food type or nutrient. Nevertheless, we envision that future large-scale epigenomic analysis of different diet groups, such as vegetarians versus non-vegetarians, may allow the construction of prediction models that have the potential to be used in forensic applications.

Is the unknown trace donor physically active?

Information on an unknown person’s physical activity levels might provide insights on their body structure and appearance, which is relevant when describing an unknown trace donor. Physical exercise can impact the epigenome [120] and regulate gene expression [121]. It is also involved in gene–environment interactions that reduce genetic effects on individuals’ body mass index (BMI) [122]. Whereas cross-sectional and case–control studies revealed no significant correlation between physical activity and global blood methylation [123], LINE-1 methylation was increased in women maintaining higher physical activity over a long period of time [124]. Exercise-related epigenetic effects were also stronger in elderly populations [125], diseased individuals (L3MBTL1) [126], and in tissues such as fat (TCF7L2) [127] and skeletal muscle (KCNQ1) [128]. Being physically fitter or exercising regularly correlates with lower cancer gene methylation in saliva [129]. When testing the effects of regular moderate exercise on inflammatory response via epigenetic changes in blood, there was no effect regarding the IL-6 [130] and p15 [131] genes, but a reduced age-dependent ASC blood methylation was observed [131]. Thus far the effects of long-term, rather than acute, exercise have been studied, so it is unknown when methylation changes are established and become detectable in relation to timing of exercise. This research is still at early stages and ongoing, but future large-scale experiments including controlled exercise regimes for study participants have the potential to identify distinct exercise-related epigenetic differences. Depending on the outcomes, a forensic tool may be developed to predict whether an unknown trace donor is physically active or not.

What is the body size/shape of the unknown trace donor?

While predicting categorical externally visible characteristics such as eye and hair color is already established [132], predicting dimension-based features, such as body height, is challenging due to their continuous quantitative nature. Although the genetic component of body height is large [133, 134], environmental factors explain about 20% of height variation. Due to the immense genetic complexity of height, despite very large genome-wide association studies (more than 250,000 subjects, the identified SNPs do not explain more than 27.4% of the phenotypic variation [135, 136]. For BMI, however, this figure is just 2.7% [137]. There is increasing evidence that epigenetic variation might play a role in shaping body height [138] and BMI [139]. The first study in humans identified that 83% of height-associated genes contain promoter CpG islands linked with gene regulation, half of which had significant DNA hypermethylation modules [138]. While there is currently no published EWAS for height, studies in other species such as Arabidopsis thaliana [140], ants [141], and sheep [142] have identified height-associated methylation in body-size-related genes. In the case of BMI, where EWASs have been carried out for humans [139], birth-weight discordant twins did not show significantly different epigenome-wide profiles [143], but three CpGs (in the gene HIF3A) were found to be significantly associated with BMI in a larger cohort of unrelated individuals [139]. For every 10% methylation increase of cg22891070, BMI was approximately 3% higher [139] however, these effects were not replicated in adolescents [144]. Following a comprehensive scan of about four million CpGs, four BMI-associated variably methylated regions (PM20D1, MMP9, PRKG1, and RFC5) were discovered [16]. In the largest meta-analysis to date, the BMI-associated DNA methylations levels for 187 loci were successfully replicated in multiple tissues and ethnic groups [145]. In another study in CD4+ T cells, eight additional BMI- and waist circumference-related CpGs were identified [146]. We envision that currently identified CpGs, together with future outcomes from large-scale epigenetic studies, may form a suitable marker pool for a future forensic tool to predict a person’s body height and weight, which in combination with physical activity information can create a more detailed picture of the physique of an unknown individual.

In which geographic region does the unknown trace donor live?

Predicting biogeographic ancestry via small sets of genetic markers is feasible in current forensic testing, at least at the continental level [4]. However, the geographic regions where the ancestors of a person originate from is not necessarily the same as the region where the individual lives (residency), especially in the current age of globalization [147]. Currently, residency can be inferred via isotope analysis [148], but this is unsuitable to crime scene traces. Genetic geographic population substructure, which is the basis of genetic ancestry inference, is caused by human migration and positive selection via local genetic adaptation to environmental factors, which occur over large periods of time involving multiple generations. By contrast, epigenetic geographic population substructure influenced by local environmental factors is produced much more quickly, and within a person’s lifetime. Giuliani et al. proposed that the factors influencing spatial epigenetic variation are mainly nutrients, UVA exposure, and pathogens [149]. Distinct epigenetic changes due to chronic sun exposure have been found in human skin (KRT75) [150], while environmental chemicals such as cadmium exposure via soil in Thai populations [151] and phthalate exposure via household products in the USA [152] affect gene-specific DNA methylation. Apart from metals and organic pollutants [153], others such as water contaminants and airborne pollution could have similar effects. Lifetime exposure to undesired disinfection products formed during water treatment caused methylation differences in 140 CpGs in Spanish individuals [154], while mitochondrial DNA (mtDNA) methylation was altered in Italian steel workers due to their high exposure to metal-rich particulate matter [155]. Nevertheless, these mtDNA methylation changes are considered minute, since overall mtDNA methylation seems to be less than 6% [156]. Overall, we regard it as likely that, besides biogeographic ancestry information from genetic markers, additional residence information via epigenetic profiling will become available in the near future with additional benefits for investigative use.

Are there hints about the socioeconomic status of the unknown trace donor?

Socioeconomic status (SES) is often measured as a combination of education, occupation, income, and marital status, thus viewed as a continuous variable it is conceptualized as the social class of an individual, associated with behavioral features and disease risks [157,158,159]. While complex and highly variable, information about the SES of an unknown trace donor could help police target their investigations. Together with genetics and physical environment, social factors also impact on epigenetic variation [160]. Well-defined epigenetic patterns have been linked to both childhood and adulthood socioeconomic environment [161]. Early-life SES was found to be associated with altered methylation in three CpG sites in blood, but the methylation effects were low (<5%) [162]. Following candidate gene approaches in multiple populations, SES-associated methylation was also reported in stress-related (AVP, FKBP5, OXTR) and inflammation-related (CCL1, CD1D, NFATC1) genes [163, 164]. In another study, low-SES was also linked with altered methylation of the serotonin transporter gene [165, 166]. Looking at global DNA methylation and job status in particular, manual workers demonstrated 24% global hypomethylation compared with non-manual workers [167]. Various SES-associated factors, including family income at birth [168], adult education [168], maternal education [169], parenting [170], and status of single parent family [168], have all been linked with altered methylation at specific genomic locations. While this research is still ongoing, following comprehensive characterization of SES-associated effects it might be possible in the near future to be able to translate an individual’s epigenome into clues regarding their educational, occupational, and marital status however, distinct predictions might be unlikely.


AI in our factories: is there a ghost in the machine?

Banking, insurance, energy networks and now justice: AI is everywhere and we don’t even notice it. What about in the industrial world? Is there still a captain on board there?

Tellmeplus knows well the situation, as they focus on AI & big data for Manufacturing, bringing predictive intelligence at the edge and inside industrial assets to increase operational performance.

“AI is a trend across nearly all industries today and yet, industrial robots have been a part of factories since the 1960s. So what’s different now? AI is playing a significant role in factories, facilitating industrial automation, reducing operational costs and defaults, optimizing process effectiveness, ensuring 24x7 production and guaranteeing equipment uptime.

How does AI make a difference? There is no ghost or magic spirit inside machines! AI simply uses data to better organize factories in order to make supply chain, design team, production line, and quality control more coordinated, more able to provide personalized products or services to customers, to make sure this product/service is always available and delivered as you expected.

This is now possible because the factories, and all machines inside the factories, are equipped with connected IoT (sensors, etc.) that collect data and act upon it. This data can be centralized and processed in the cloud, in IoT platforms like Microsoft Azure IoT… or directly inside machines — at the “edge”, making it more reliable and reactive. This is when AI truly becomes “the ghost in the machine”!


Fine-Tuning

What makes today&rsquos deep neural nets at once powerful and capricious is their ability to find patterns in huge amounts of data. Loosely modeled on the human brain, these complex computing systems are the not-so-secret sauce of the current AI boom. They are why digital assistants like Apple&rsquos Siri and Amazon&rsquos Alexa have gotten very good at recognizing speech, and why Google translations are finally comprehensible. They also enable machines to identify images, predict diseases and beat humans at the television quiz show, Jeopardy!, and at go, a game arguably more sophisticated than chess.

Neural nets process information by passing it through a hierarchy of interconnected layers, somewhat akin to the brain&rsquos biological circuitry. The first layer of digital &ldquoneurons&rdquo&mdashcalled nodes&mdashreceives raw inputs (such as pixels in a photograph of a cat), mixes and scores these inputs according to simple mathematical rules, and then passes the outputs to the next layer of nodes. &ldquoDeep&rdquo nets contain anywhere from three to hundreds of layers, the last of which distills all of this neural activity into a singular prediction: This is a picture of a cat, for example.

If that prediction is wrong, a neural net will then tweak the links between nodes, steering the system closer to the right result. Yann LeCun, director of AI research at Facebook, likens this web of numerical connections to a box with millions of knobs. By tuning the knobs to satisfy millions of examples, the neural net creates a structured set of relationships&mdasha model&mdashthat can classify new images or perform actions under conditions it has never encountered before.

That process, known as deep learning, allows neural nets to create AI models that are too complicated or too tedious to code by hand. These models can be mind-bogglingly complex, with the largest nearing one trillion parameters (knobs). &ldquoWhat&rsquos cool about deep learning is you don&rsquot have to tell the system what to look for,&rdquo says Joel Dudley, director of Biomedical Informatics at Icahn School of Medicine at Mount Sinai in New York City. &ldquoIt&rsquos just, &lsquoHere&rsquos a few million pictures of cats. You figure out what a cat looks like.&rsquo&rdquo

This flexibility allows neural nets to outperform other forms of machine learning&mdashwhich are limited by their relative simplicity&mdashand sometimes even humans. For instance, an experimental neural net at Mount Sinai called Deep Patient can forecast whether a patient will receive a particular diagnosis within the next year, months before a doctor would make the call. Dudley and his colleagues trained the system by feeding it 12 years&rsquo worth of electronic health records, including test results and hospital visits, from 700,000 patients. On its own Deep Patient then discerned hidden harbingers of illness. &ldquoWe showed it could predict 90 different diseases, ranging from schizophrenia to cancer to diabetes, with very high accuracy&mdashwithout ever having talked to an expert,&rdquo Dudley says.


Artificial Intelligence — Human Intelligence Exhibited by Machines

King me: computer programs that played checkers were among the earliest examples of artificial intelligence, stirring an early wave of excitement in the 1950s.

Back in that summer of ’56 conference the dream of those AI pioneers was to construct complex machines — enabled by emerging computers — that possessed the same characteristics of human intelligence. This is the concept we think of as “General AI” — fabulous machines that have all our senses (maybe even more), all our reason, and think just like we do. You’ve seen these machines endlessly in movies as friend — C-3PO — and foe — The Terminator. General AI machines have remained in the movies and science fiction novels for good reason we can’t pull it off, at least not yet.

What we can do falls into the concept of “Narrow AI.” Technologies that are able to perform specific tasks as well as, or better than, we humans can. Examples of narrow AI are things such as image classification on a service like Pinterest and face recognition on Facebook.

Those are examples of Narrow AI in practice. These technologies exhibit some facets of human intelligence. But how? Where does that intelligence come from? That get us to the next circle, machine learning.


Brain Activity Identifies Individuals

Kerry Grens
Oct 12, 2015

This image shows the functional connections in the brain that tend to be most discriminating of individuals. Many of them are between the frontal and parietal lobes, which are involved in complex cognitive tasks. EMILY FINN Neuroscientists have developed a method to pick out an individual solely by his connectome&mdasha pattern of synchronized neural activity across numerous brain regions. Researchers had observed previously that brain connectivity is a unique trait, but a new study, published today (October 12) in Nature Neuroscience, demonstrates that neural patterns retain an individual&rsquos signature even during different mental activities.

&ldquoWhat&rsquos unique here is they were able to show it&rsquos not just the functional connectivity&mdashwhich is how different brain regions are communicating over time when you&rsquore not doing a specific task&mdashbut even how the brain is activated during a specific task that is also very fingerprint-like,&rdquo said Damien Fair, who uses neuroimaging to study.

Fair and others said individuated brain scans could be applied to better understand the diversity of mental illnesses often lumped into the same diagnosis. “We don’t kneed to keep going at the average. We have the power to look at individuals,” said Todd Braver of Washington University in St. Louis who did not participate in the study. “To me I find that really exciting.”

The research team, based at Yale School of Medicine, extracted data from the Human Connectome Project, which includes functional MRI (fMRI) data from about 1,200 people so far. The Yale team analyzed imaging data from 268 different brain regions in 126 participants. To create a connectome profile for each individual, the researchers measured how strongly the activity of a specific brain region compared to the activity of every other brain region, creating an activity correlation matrix.

Each person, it turned out, had a unique activity correlation matrix. The team then used this profile to predict the identity of an individual in fMRI scans from another session.

Depending on the type of fMRI scan assessed, the researchers could nail someone’s identity with up to 99 percent accuracy. Scans taken during mental tasks, rather than resting, made it more difficult, and the accuracy dropped to below 70 percent.

“Even though brain function is always changing, and we saw it’s slightly harder to identify people when they are doing different things, people always looked most similar to themselves” than to another participant, said Emily Finn, a graduate student in Todd Constable’s lab and the lead author of the study.

Fair pointed out that one of the most individualized brain regions is the frontoparietal cortex, which helps to filter incoming information. He has found the same result in his own work on fingerprinting connectivity. “It really seems important for making an individual who we are,” he said.

The ability to identify individuals even during tasks on different days would be important for clinical applications. Mental disorders are often classified by phenotype, or symptoms, that may represent a variety of underlying causes. “These types of technologies I think are going to help us personalize mental health better,” Fair told The Scientist. “We’ll have more information to say specifically what’s happening in your brain.”

Finn’s group was also able to associate a person’s connectome with his or her “fluid intelligence.” This trait is measured by asking people to solve a problem or find a pattern without using language or math skills or learned information. Finn told The Scientist that stronger connections between the prefrontal and parietal lobes, brain regions already known to be involved in higher order cognition, were most indicative of higher fluid intelligence scores. The results “suggest levels of integration of different brain systems are giving rise to superior cognitive ability,” she said.

“It’s not just this idiosyncratic fingerprint that they’re talking about that basically allows you to differentiate one individual from another,” Braver said of the study, “but it pushes the idea that [the connectivity signature is] functionally relevant, that those things may be related to things that we think are interesting individual differences, like intelligence.”

Finn cautioned that the intelligence correlation is more a proof of concept to link brain connectivity with behaviors, rather than something having real-life applications. “Hopefully, we could replace that with some variable, like a neuropsychiatric illness or [predicting] who’s going to respond best to some treatment.”


Summary

Creating novel industrial processes to reduce climate change? DARPA had that covered too. So while Dr. Ling made sure to remind the audience up front that this was all about supporting warfighters, it was impossible to not consider the deeper implications of what was being presented as the event proceeeded.

The reality is that the true DARPA mission isn’t just about war. A happier, more secure and sustainable world is the best possible security for the United States, a fact that DARPA’s leaders seemingly recognize at the moment. And so DARPA is developing technologies for rapid identification of communicable diseases, restoring lost biological functions, producing materials and developing novel industrial processes to prevent slow and reverse climate change, save ecosystems and more.

And DARPA’s next revolution, biology is technology, is something even bigger than the Internet. They’re out to revolutionize the practice and products of bio-science and along the way they are re-defining what it will mean to be human. Will we alter our biology to enable direct mind to mind communication? Can we extend our immune system into the world to cure all communicable diseases? Can we cure and repair the most damaging and persistent mental illnesses?

In this amazing two day event, DARPA opened the door to a wider public collaboration and conversation about these amazing ideas.

A second event is planned for New York City in June and video of the February presentations will be available online according to DARPA representatives at the event. I will update this story with videos when they are available.



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