Music is universal. Every human society has experienced and nurtured collection of sounds through vocal and instrument methods to generate emotions. Though humans experience music and respond to beats, neural mechanisms responsible for generating emotions in response to sound waves, is not well understood. Studies show a correlation between brain circuits and reward systems that program a person towards repetition of experience.
To fill a certain “molecular link” between sound waves and emotions, the science of musicality has found increased attention among researchers. Before moving further, let us pause for a moment and define the term music itself! Given that music finds evolutionary presence, from birdsongs to elephant orchestras to human symphonies, it’s important to define music and separate it from noise. Also, one needs to understand music and musicality distinctly.
Music is essentially an arrangement of sound vibrations characterized by pitch, rhythm, and harmony. Musicality is an inner trait that generates sensitivity and/or knowledge of music i.e., receptivity, and creativity of music.
The biological basis of musicality is an interesting offshoot of neuroscience research and deals with molecular correlates of music perception and response. In the animal kingdom music is frequently used in mate selection, parental care, and synchronization of group tasks.
Did the co-evolution of speech and language generate unique music-motifs at the genome level? We do not know.
Ethnomusicology deal with the study of music traditions across various cultures in search of specific patterns e.g., rhythm, timbre, and pitch. Studies reveal that melodies are usually made of a few discrete pitches over a scale divided into small intervals. One would expect to find greater variability in music patterns across the geographies. However, the variation seems to be far less than expected, indicating common physiological correlates in a diverse population.
Studies have shown that the motor corticobasal ganglia–thalamocortical circuit (mCBGT) plays an important role in human music perception and performance. It has been observed that birds are more sophisticated than humans in detecting absolute pitch. Humans tend to find relations among pitches rather than enjoying absolute pitch levels.
Recently, researchers from Vanderbilt University reported a strong association of genetic variants with the ability to respond in synchrony with musical beats (Niarchou et al 2022). This provides yet another compelling evidence in favor of musicality encoded in the human genome.
In this study, a total of 69 genes were found to be synchronously associated with a periodic pulse called a beat. Some of these genes were involved in brain development, auditory and motor skills. Thus, rhythmic response to musical beats in the form of dancing or tapping, or humming seems to have some genetic basis, probably involving a large set of genes. A strong correlation between VRK2 gene (previously associated with behavioral and psychiatric conditions) with beat synchronization has been reported, indicating a link with neural development.
The heritability of this 69 gene set was found to be moderate and resembled the heritable pattern of other complex features. However, it was interesting to observe the enrichment of beat synchronization imprints in the cerebellum, prefrontal cortex, inferior temporal lobe, and basal ganglia nuclei, thereby indicating genetic contribution to musical perception.
Identification of a gene set involved in generating a sense of musicality opens a new window of opportunity to study cultural signatures of the “musical gene group” (MGG). Identification of MGG may lead to restoration of developmental speech-language disorders and rehabilitation of affected people.
Overall studies exploring genomic origins of musicality uncover neural mechanisms responsible for musical beat perception. Given that musicality involves synchronization of breath, emotions, and muscular movement, it would be interesting to understand how triggering an MGG affects a sense of musicality. An interesting study points to a strong correlation of the locus 4q22 in musically related traits (Pulli et al 2008).
Large-scale GWAS (Genome-Wide Association Studies) are required to understand the expression of polygenic molecular markers in different cultural contexts. It would be interesting to know if these genomic variants heritable? Do they get enriched in families with a strong musical tradition? Can one engineer a ‘non-musical’ genome towards a musical genotype?
The need of the hour is an integrated musicality theory that takes into consideration genomic, anatomic, and cultural data.
Anger is an intense emotional state triggered by a strong sense of pain or threat. It is an ‘emotional cloudburst’ that can make a person feel momentarily assertive but consumes enormous energy, in the process. For anger to move from unhappiness to rage, a person needs to demonstrate extreme dislike along with a compulsive behavior. Aggression is a higher level of disturbance that usually comes in the form of hostile reactions or physical violence. Some of the physiological manifestations of anger include increased heart rate, blood pressure, and rise in adrenaline levels.
Neuroscience offers key insights into the world of emotions. Evolutionary neuroscientists study nervous system, from lower to higher organisms, to find functional outcomes of neural connectivity patterns. Behavioral neuroscience, a prominent branch of neurosciences, deals with the physiological, genetics, and developmental aspects of the brain vis-à-vis phenotypic outcomes. As anger involves physiological, cognitive, subjective, and behavioral ingredients, a formal study of anger merits a multidimensional exploration.
Several studies point to an increase in behavioral complexity vis-a-vis an evolutionary increase in brain size. Though the gender-specific manifestation of anger and aggression is a common observation, underlying neural mechanisms governing such an aberrant behavior are incompletely understood.
The study of anatomical substructures in the brain, versus emotional outcomes, is an interesting area of research. The impact of insular ‘gray matter volume’ and ‘functional connectivity’ has received increased attention of late with data indicating a distinct correlation between GMV and aggression. However, more work is needed to uncover cellular mechanisms responsible for generating anatomical structures and aberrant behavior.
The insular cortex of the brain (also known as the insula) is a deep region of the cerebral cortex believed to be involved in generating emotions. Studies show that men exhibit greater insular connectivity compared to females. Could that be one of the reasons for higher male aggression? Does insular connectivity have a genetic basis? There is no clear understanding yet.
The role of the activated anterior insula in cognitive regulation, self‐control, and aggression seems to be affected by the social environment. Studies have associated the mid-posterior insula region with aggression. However, the behavioral outcome of connectivity patterns within the mid-posterior insula is unclear.
Studies conducted on rats to find molecular reasons for ‘anger suppression’ versus ‘anger expression’, has uncovered a general body weight marker in the ‘Anger group’ in comparison with the ‘Control group’. The ‘anger-expressing’ group shows a marked decrease in body weight than the ‘anger-suppressing’ group. At the molecular level, changes in mRNA expression of 5-Htr2C, GABABR2, and 5-Htr3B genes have been observed which increases a future possibility of treating emotional disorders through molecular interventions.
Serotonin is a chemical messenger that moves between brain cells and the rest of the body. It plays an important role in mood variations, sleep, sexual desire, and so on. Low/high levels of serotonin can cause significant physical and psychological issues. In a recent study, variants of the Tryptophan hydroxylase (TPH) gene, A218C, and the A779C were found to be associated with anger and aggression. Previously, the TPH enzyme has been linked to serotonin regulation.
Studies indicate a link between DARPP-32 (dopamine- and cAMP-regulated phosphoprotein, 32 kDa), and antisocial behavior, drug addiction, and schizophrenia. Data also suggest the role of the amygdala in anger. Furthermore, some of the recent work shows a possible ‘immunological link’ between depression and anger. A weak correlation has been reported between IgA levels, natural killer cells, and anger expression patterns. Nevertheless, expression of anger (in contrast to suppression of anger) has been significantly associated with variations in the IgA levels. Studies indicate that depression causes a reduction in the number of Natural Killer cells and an increase in the levels of IgA. Experimental evidence suggests the downregulation of wound healing due to anger!
While all these studies attempt to find a mechanistic link between anger at the level of tissue and molecules, genetic susceptibility to anger is still a nascent area of research. Studies suggest that signaling pathways involved in intracellular calcium homeostasis may play a role in determining susceptibility toward anger. Molecular biology of anger is a fascinating area of research with direct health applications.
Addiction is an obsession characterized by a lack of control and harmful consequences. Historically, the term addiction has been associated with smoking, drugs, alcohol, and gambling but in recent times, addiction has been found increasingly connected with habits related to the overuse of computers, cell phones, shopping, solvent consumption, and so on. In theory, it is possible to be addicted to anything living or non-living. All that’s needed is a compulsion.
Before moving further, it may be relevant to differentiate addiction from passion. Passion is a conscious and intense involvement that leads to an enriching outcome and a sense of purpose. In contrast, addiction is an extreme compulsive involvement that’s self-harming and results in a deteriorating outcome. Evidence suggests an evolutionary angle to addiction - addiction is not restricted to humans only.
Though addiction leads to huge mental, social, and economic consequences, its etiology, preventive measures, and customized treatment requires more work.
Genetic reasons contributing to increased sensitivity towards addiction are multifactorial i.e., determined by several genes. Estimates indicate that up to 60% of population variability found in addicted individuals is attributable to genetic factors.
Some of the key questions that merit deeper study are: Is addiction heritable? If yes, are some people more prone to addiction than others? Is withdrawal from addiction affected by genetic factors? Why some people are naturally resistant to addiction while others easily fall into the vortex of compulsive behavior?
Addictive alkaloids, such as nicotine, cocaine, or cathinone, are commonly referred to as “human drugs of abuse”. Studies have found mapping of specific neurocircuitry to the addiction of abusive drugs e.g., (i) binge/intoxication — basal ganglia (including ventral tegmental area and nucleus accumbens) (ii) withdrawal effects — extended amygdala and habenula and (iii) craving (anticipation) — prefrontal cortex (PFC), insula, and allocortex. With time, addiction alters the brain’s reward circuitry leading to compulsive drug seeking and makes the situation worse.
Genome-wide association studies (GWAS) provide an interesting layout of DNA hotspots that may have answers to the “addiction phenotype”. In the GWAS domain, a term QTL is used as a short form of “Quantitative Trait Loci”. These are not products of single genes but a collection of gene expressions determining a certain trait. Chromosomal loci that explain variance in expression traits are called eQTLs (Expression Quantitative Trait Loci). eQTLs located near the gene which produces RNA or protein is referred to as cis-eQTLs. By contrast, those located often on different chromosomes, are called trans-eQTLs.
One of the earliest GWAS conducted for nicotine addiction, implicated genes on the long arm of chromosome 15 (15q25, nicotinic receptor genes, CHRNA5-CHRNA3-CHRNB4) and the short arm of chromosome 8 (8p11, CHRNB3-CHRNA6). Unexpected genetic variant associations have shown up in the cerebellum between smoking- and alcohol-associated (cis-eQTL) SNPs.
SNP (single nucleotide polymorphism) is a type of single DNA base variation normally observed in more than 1% population. They make a ‘unique genomic ID of a person’ and may be associated with disease conditions in rare situations. In contrast, mutations are found in less than 1% population and may lead to strong and heritable genetic defects.
Studies have identified 11 genetic loci for smoking, 8 loci for alcohol, and 2 loci for illicit drugs combined. Addiction phenotypes and anxiety have been shown to be heritable with significant quantitative trait loci for drug dependence (14q13.2-q21.2) and a broad anxiety phenotype (12q24.32-q24.33). Similarly, positive genetic correlations have been observed between anxiety and alcohol (9q33.1-q33.2) and drug dependence (18p11.23-p11.22).
Genetic mapping of anxiety traits to the long arm of chromosome 12 i.e., q24, and anxiety-alcohol association with 9q33 are interesting leads that require further research. The transcription factor ΔFosB is induced severalfold by chronic drug exposure and has been observed to increase addiction. Likewise, altered expression of G-protein signaling 3 (AGS3) and brain-derived neurotrophic factor (BDNF) has been reported in the system several weeks after the withdrawal.
GWAS for cigarette smoking (nicotine dependence) has led to the identification of several genetic loci suggesting the polygenic nature of addiction i.e., involving multiple genes. However, an understanding of neurobiological pathways leading to addiction is still open for exploration. Genetic variants implicated in nicotine addiction and withdrawal are not solo performers. Complex molecular interactions are usually reported in such conditions.
In future, integration of the GWAS data with Omics data generated from single cells of addiction-affected brain tissue may lead to novel associations. Some of the studies have shown functional missense SNPs with the potential to alter susceptibility towards nicotine dependence or substance use through perturbation of gene regulation.
The role of external environmental factors e.g., stress, sleep, and mood variation in impacting addiction, is an open question. Studies have found interactions between the circadian genes and other biomolecular pathways affecting individual sensitivity toward substance use. Circadian rhythms are 24-hour cycles of the body’s internal clock over which our activities are mapped. Researchers have identified certain circadian genes as risk factors for addiction.
With an increase in addiction to substances, certain brain cells show epigenetic adaptation contributing to the long-term behavioral abnormalities that define addiction. The epigenetic process goes beyond a vertical transmission of information from genes to proteins.
Epigenetics defines the contextual impact on gene expression that is not hardwired in the genetic code itself. Of several epigenetic possibilities, histone protein modifications in specific regions of the brain have been found to correlate with addiction. Histones are positively charged proteins associated with DNA that help in stabilizing the DNA structure and are useful during coiling and uncoiling.
Researchers have used the term psychogenomics to connect genomics and proteomics data with behavioral outcomes. It is expected that psychogenomics will eventually connect all the levels from molecules to phenotypes leading to (a) the identification of addiction vulnerability genes and (b) the identification of molecules that contribute to the regulation of reward, motivation, and cognition in an addicted brain.
Some of the questions that merit close attention are:
Which neural mechanisms responsible for transition from a recreational drug to a chronically addicted state? Why is there a persistence of addictive behavior even after giving up drug use?
In the future, understanding the molecular anatomy of an ‘addicted brain’ versus a non-addicted one, will open several possibilities for early diagnosis and treatment. Understanding and targeting epigenetic modifications may improve our mechanistic understanding of addiction and open new avenues of intervention. Enormous work is needed to enhance our understanding of the genome and associations with neurobiology pathways underlying addiction leading to timely interventions and a decrease in the public health burden.
Depression is a mood disorder that causes a deep feeling of sadness, emptiness, and a disconnect from surroundings. Depression can range from minor to major manifestations and result in a significant drop in a person’s capacity to function.
Depression has been ranked by WHO as the single largest contributor to global disability. Statistics indicate that over 300 million people globally are likely to live with depression. Estimates indicate that adolescents with a major depressive disorder are up to 30 times more likely to commit suicide. Given that depressions occur due to a heterogenous mix of etiologies and exhibit a wide range of severity, one needs to treat depression based on individual reasons and manifestations. Questions arise when one studies the genetic transmission of depression, aided by environmental factors.
1. Is there a genetic transmission and vulnerability to depression?
2. Which chromosomal regions are hotspots of depression, if any?
3. Given a huge variety of contributory factors, is there a common set of metabolic and regulatory pathways triggered in depression?
4. Can the severity of depression be predicted by studying DNA sequence and/or gene expression?
5. Is it possible to develop pre-symptomatic detection molecular kits for mood disorders or psychiatric illnesses?
Depression is a complex polygenic disorder involving several genes with nearly a third of the cases, showing heritability. Genome-Wide Studies (GWAS) and Transcription Wide Studies (TWAS) have been used to explore the patterns of genetic inheritance.
Several challenges exist in finding patterns in depression genetics. One is the polygenic nature of the disease itself that prompts one to employ association statistics on a large GWAS canvas to find a reliable risk score. Second, is the high lifetime prevalence of depression. Data indicate that women are likely to experience major depression, with one in three women and one in five men likely to experience major depression by the age of 65. Interestingly, in affluent societies and high-income countries, the risk of major depression has been found to be higher.
Organizations like Psychiatric Genomics Consortium (PGC), CHARGE (Cohorts for Heart and Aging Research in Genomic Epidemiology) Consortium, CONVERGE (China, UK, US) and SSGAC (Social Science Genetic Association Consortium) have organized large-scale studies on the genetic basis of depression, leading to interesting data.
Several chromosomal loci have been found to be involved in Major Depression Disorder, with several putative genes showing associations with major depression e.g., SIRT1, LHPP, KSR2, DCC, VRK2, NEGR1, RERE, SORCS3, OLFM4, PAX5, and FHIT.
In recent years, high throughput transcriptomics technologies have enabled researchers to move beyond genome associations of psychiatric disorders and look for contributory factors beyond mendelian genetics.
Epigenetics is a process that impacts gene expression and translation without affecting the standard transmission process of genetic traits. Epigenetic modifications include DNA methylation, microRNA and histone modifications.
Several candidate genes have been identified in mood disorders / psychiatric illnesses e.g., (i) DNA methylation: RUFY3, GBBR2, BDNF, WDR26 (upregulated), GRIK2, BEGAIN (downregulated), (ii) Histone Modifications: H3K14ac, HDAC2, H3K4ac (downregulated), (iii) microRNAs: miR-1202 (downregulated). There is a pressing need to study RNA methylation vis-a-vis mood disorder manifestations.
Measuring the severity of Major Depression is challenging, as data is generally speculative and uncertain. It is unclear if the severity should be measured in terms of the range of symptoms, the number of episodes, their duration, other features, or their weighted combination
Genetic patterns and epigenetic modifications are end‐point observations — the key is to find the associations between genetics, epigenetics, and environment and establish biological underpinnings that can be used for an effective and early diagnosis, prevention and treatment. Research on neurodevelopmental disorders may provide insight into how genetic factors can cause aberrant epigenetic processes.
Love is defined as a warm, deep, and selfless affection arising out of attachment. It comes in various forms. For example, love can be an expression of deep family attachment or an intense romantic feeling that creates exhilaration and euphoria. The feeling of love ‘immobilizes’ extraneous thoughts orienting a person in a single direction.
The subject of love has been studied psychologically and biochemically. On diving deep into the life-operating-system itself, one finds that Love is NOT an act. It is a certain quality of mind i.e., one can be in love with the existence itself (deep philosophy of life) or choose a specific person for the expression of thoughts and emotions.
Using functional MRI technology neurobiologists have asked questions about neural correlates of love and have reported interesting correlations from the brain scan images. Several love hotspots have shown up in the brain, over a large possible neural terrain. These areas seem to generate a strong reward circuit whenever a strong feeling of love is generated. Let us examine the data under different headings.
Neuroanatomy of love: Studies indicate that brain has a brilliant strategy to experience an uninterrupted feeling of love. Areas that generate a highly fulfilling reward system are activated and areas that generate fear, logic, and judgment, are deactivated.
Key areas that have been associated with the “brain reward circuit” with a strong feeling of love, are:
Ventral Tegmental area representing a group of neurons located close to the midline on the floor of the midbrain (ventral tegmental area).
Anterior cingulate cortex (ACC) — positioned above and adjacent to the corpus callosum, resembles a collar. Its unique position helps connect the emotional limbic system and the cognitive prefrontal cortex.
Nucleus accumbens. It is located in the basal forebrain. There is a nucleus accumbens in each cerebral hemisphere and is considered part of the basal ganglia and ventral striatum
Orbito-frontal cortex: This region is located in the frontal lobes of the brain and has been found to be associated with many functions, including short-term memory, personality expression, social behavior, and speech
Hypothalamus: This region is associated with sexual arousal and is only involved in romantic love.
Amygdala: The amygdala is known to be engaged during fearful situations and its deactivation, is important for love to emerge.
Hippocampus. It is a complex structure embedded deep into the temporal lobe and plays a major role in learning and memory. Along with other areas listed above, Hippocampus forms a core group responsible for generating a reward system.
The key difference between romantic and maternal love is in the activation of specific regions of the brain.
Neurochemistry of love
When we fall in love, certain chemicals flood our brains leading to strong emotional and physical responses. It is important for the brain to suspend critical judgment at the time of love. Studies have identified three major actors: dopamine, oxytocin, and vasopressin.
Dopamine is the primary pleasure-generating molecule of the brain reward circuitry. The release of dopamine generates a feel-good factor both for mental and physical involvement in romantic love.
Oxytocin, a peptide produced in the hypothalamus, sometimes also called the ‘love hormone’, as it’s specifically linked to the feelings of attachment. As a chemical messenger in the brain, oxytocin is involved in physical intimacy, trust, romance, and parent-infant bonding.
Vasopressin is a hormone secreted by the pituitary gland and performs several roles, one of which is emotional stabilization.
Both oxytocin and vasopressin are produced by the hypothalamus, stored in the pituitary gland, and released into the blood at the peak of sensual feelings and also during pregnancy and breastfeeding.
It seems dopamine and vasopressin are essential for a man to fall in love, whereas for women, the key love chemicals are oxytocin and dopamine.
In both genders, love receptors are distributed in parts of the brain and activated during the process. Interestingly, in long term love relationships cortisol and serotonin concentrations return to the normal level. However, dopamine is still actively produced in the background thereby removing the stress while maintaining the intensity.
The brain has been found to keep logic, critical judgement, fear and anger suppressed during love by deactivating amygdala (seat of fear and anger) and frontal cortex (seat of logic and judgment). One wonders if the suppression of logic centers in the brain justifies the statement “love is blind”
Neuroanatomy and neurochemistry studies have started revealing the fascinating mechanistic basis of love. However, a lot more work is needed to understand the neurochemistry of love when it fails (leading to breakup) or is loyally maintained in extreme hardships.
Modern concepts and lifestyle have generally decreased the attention span on a single person. Previously, love used to be a single-person-centric experience lasting the entire lifetime. However, frequent breakups, multiple pre-marital and post marital affairs have become commonplace now-a-days, along with addiction to substances. It would be interesting to find how brain rewires itself in such situations. In view of transient emotional associations and changing perspectives on love, how neuroanatomy and neurochemistry will respond in future, remains an interesting question.
Studies are also needed to understand the neural hardware and software involved in elevating and maintaining love at the cosmic level where it doesn’t recede to a person centric.
Human communication has evolved from gestures to language, using verbal and non-verbal approaches e.g., speech, writing, visual expression, and so on.
Verbal communication occurs when vocal cords generate distinct sound patterns that we call speech. Speech is an expression of thoughts enabled through a method called language that enables reading, writing, and memorizing.
It is important to note that speech and language are not the same. A child may understand the language (of others) but may be unable to speak.
Neuroscientists have found a distinct phase transition in the ancestral hominid brain structure who graduated from a protolanguage to a formal language.
This transition was significantly marked by the appearance and development of the cerebral cortex.
Several candidate genes have been identified to help in the development of brain and language processing e.g., CCR2 CCR7 CCRL2 CLEC2D CXCR4 DHFR genes. Interestingly, these are viral in origin. Likewise, non-viral transferred core genes involved in vocal development are AHNAK,RUNX2, FOXP2, and so on.
Overall, genes that control brain size (e.g., ASPM and MCPH1) and genes that control vocal development (e.g., FOXP2), and genes that control laterality (e.g., PCHI1 1X) collaborate to make the brain language ready.
Of all the regions of the brain, the Thalamus, seems to be central to language and human cognition. From the neanderthal (pre-language) brain to the (language-ready) Homo sapiens brain, there have been anatomical upgrades in the frontal lobe, parietal lobe, and fronto-parieto-thalamic network.
Given that languages are continuously evolving and have been significantly impacted by technology, few questions arise here:
How is genomic investment into language decided and adjusted over geographies with a wide range of language variations?
Do language pathologies have a distinct basis in the form of DNA mutations and epigenetic modifications?
Was there a ‘universal grammar’ that predated the language itself?
Can sound waves (in verbal communication) be used as a basis for measuring evolutionary complexity of organisms?
Given that human communication has been significantly impacted by technology, which brain alterations and adaptations are likely to come up in the future?
Driven by cultural and language influences, does neural wiring have a core conserved domain supported by culture specific motifs?
Does gene-cultural co-evolution determine a certain language ability or is cultural influence a dominant force in gene-language evolution?
Given that viral infection has been thought to subtly contribute to cognitive developmental disorders, has viral infection or its integration in the human genome impacted language abilities?
Does the horizontal transmission of genes or the metagenomic status of the human body impact the language outcome?
While these and many more questions may find answers in future, one observes amazing coordination among the auditory system, memory, vocalization, and motor skills that enables effective verbal communication. The emerging field of biolinguistics deserves greater attention with a hope to fill in the missing link between the origin, diversity, and coordination among genes to build a language-ready nervous and motor system.
The brain is an evolutionary marvel endowed with a phenomenal capability of managing the most sophisticated technology on the planet — The Human Being!
Recent experimental data obtained from the brain chemically liquified into cellular soup indicates an inventory of ~ 86 billion neurons, as against earlier estimates of 120 billion or so. Numbers get interesting on comparing the fruit fly brain (100K), mouse brain (75 million), and elephant brain (250 billion). Clearly, a numerical neural advantage is not the basis for generating the range and intensity of memory and intelligence.
Some of the recent back-of-the-envelope calculations indicate that the human brain may be endowed with a space for 2.5 petabytes of memory (1 petabyte is equal to a million gigabytes) i.e., 2.5 million GB of digital memory. Computational neuroscientists tend to put these numbers in the range of 100 TB by assuming a scenario of 100 billion (total neurons) with each neuron having 1000 synapses on average and 1 data point stored per synapse.
The enormity of cerebral storage can be appreciated by the fact that if our brain worked like a digital video recorder, it’s capable of storing the data generated by continuously watching TV for 300 years without blinking the eyes! Numbers may vary, but the fact is that brain's data limits are difficult to compute, due to unclear understanding of storage mechanisms.
Biological memory comes in several forms e.g., genetic memory (transferred from parents), evolutionary memory (transferred from earlier life forms), conscious memory (intentionally generated within this life), unconscious memory (originating from background canvas of gravity, time, space) and so on.
It is not known:
How brain compiles an incoming physical data into a certain biological format?
Is brain data stored in the format assigned at the five sensory ports or does the brain use any special compression algorithm on receiving the data?
How does a brain manage an inventory of memories linked to space, time, and emotions?
Neuroscientists are of the view that the brain stores data mainly in synapses, with each synapse storing one byte or more. Interestingly, non-neural cells called glial cells (that are 3 times more abundant than neurons) have a heavy influence on neuronal activity and are believed to be involved in memory management.
Evidence indicates that the storage of data follows a path from the cortex (rich in nerve cells) to the hippocampus (the key switching point) while retrieval of the data follows an opposite direction. It is believed that during the day, data storage takes place while during sleep, data retrieval is a dominant process.
Hippocampus is an important place for indexing and storing episodic memories i.e., those that are related to personal experiences. Neocortex is the largest region of the cerebral cortex that covers the brain from outside and presents itself in the form of wrinkles. It is involved in sensory perception, generation of motor commands, reasoning, and language.
As one would expect, brain does not seem to store data in one region only. The data storage is distributed in the hippocampus (located in the brain’s temporal lobe, which looks like a seahorse), neocortex, and amygdala (an almond-shaped structure) for explicit memories (episodic — personal experience of events and semantic — the general information). Perfect coordination between the hippocampus, neocortex, and amygdala is extremely crucial in determining the stability and efficient retrieval of memory.
For the short-term storage of the data (lasting less than a minute), the prefrontal cortex seems to be the preferred destination. The control centers for unconscious memory (also called Implicit memory) seem to be the basal ganglia (deep brain structure) and cerebellum (rear base of the brain). The cerebellum plays an important role in remembering emotional experiences. Emotions activate the amygdala, which in turn facilitates the storage of information in various areas of the cerebrum.
Three types of neurons seem to be responsible for information transfer in the nervous system.
1. Sensory neurons for detecting external stimulus from the five senses and sending the data for further analysis and storage through the neuronal network.
2. Interconnecting neurons for transferring information throughout the nervous system and connecting with the motor neurons.
3. Motor neurons for connecting and activating the muscular system in case a response is required.
Whatever be the route of transmission or destination, the communication within the body appears in the form of an electrochemical signal. An electrochemical activation in one neuron triggers the release of signaling molecules called neurotransmitters for invoking a corresponding electrochemical reaction in the connected neuron. It is believed that the continuous transmission of neurotransmitters strengthens signal transmission.
Some of the major neurotransmitters are amino acids (e.g., glutamate, aspartate, gamma-aminobutyric acid, D-serine), gaseous molecules (e.g., nitric oxide), monoamines (e.g., dopamine, norepinephrine, histamine, serotonin), peptides (oxytocin, somatostatin) and so on.
When we repeatedly read something, the hippocampus gets activated to strengthen memory storage. Over time long-term memories seem to be transferred to the neocortex (a set of layers that makes up the largest part of the cerebral cortex). The memory gets strengthened if emotions are added to the observations. During emotions, the brain releases higher concentrations of neurotransmitters that strengthen the hippocampal activity.
Historically, brain cells have been thought to be non-dividing. This view satisfied the assumption that with every division the data storage ability at synapses could get affected. Interestingly, recent evidence indicates that hippocampal cells divide and generate new neuronal cells even in old age.
Understanding neural mechanisms of data storage can lead to more effective treatment of diseases like Alzheimer’s.
Some people who are in their 80s and 90s show features of memory, sharpness, and mental agility of young people. This rare group of people is called SuperAgers. Studies have shown that the brain of SuperAgers is composed of neurons that are bigger than average size.
1. Could it be that the neuronal size increase enables greater information processing?
2. If yes, what sets limits on the neuronal size in the average population?
3. What are the metabolic costs of a neuron reaching an XL size?
During early development, the brain forms more than a million new neural connections every second to become a ~ 100 billion neuronal mass with trillions of synapses. Though by age 6 more than 90% of the brain is formed, the frontal lobes (responsible for planning, working memory, and impulse control) fully develop by the mid-30s.
It is quite possible that technological advancement could enable memory prosthetics one day. Would it be possible in future to erase traumatic experiences of life by selectively purging memory dumps? Maybe one can also transfer memories among humans in the future for faster learning and adaptation.
One can only perform thought experiments in search of more fascinating scenarios. However, the way technological advancements are emerging at a fast pace, what looks like a scientific fiction at this time may become a living reality one day.
Bartels A, S. Zeki. The neural correlates of maternal and romantic love. Neuroimage. 2004: 21, 1155-66
Beauregard M. The neural basis of unconditional love. Psychiatry Res. 2009 : 172, 93-8.
Benitez-Brraco A & J Uriagereka. The Immune Syntax Revisited: Opening New Windows on Language Evolution. Front Mol Neurosci. 2016: 8, 84.
Bianca P. Acevedo. Neural correlates of long-term intense romantic love. Social Cognitive and Affective Neuroscience, 2012: 7, 145–159
Boeckx C, A. Benitez-Burraco. The shape of the human language-ready brain. Front Psychol 2014: 5, 282
CONVERGE consortium. Sparse whole-genome sequencing identifies two loci for major depressive disorder. Nature. 2015: 523, 588–91.
Farnam Alireza. Effect of Anger Patterns and Depression on Serum IgA and NK Cell Frequency. Iran J Immunol 2016: 13(1), 37-44
Forde LA. Addiction and the Role of Circadian Genes. J Stud Alcohol Drugs. 2017: 78, 645-653.
Gadi Gilam , Talma Hendler. Deconstructing Anger in the Human Brain Curr Top Behav Neurosci . 2017;30:257-273.
Gouin JP et al. The influence of anger expression on wound healing. Brain Behav Immun 2008: 22(5):699-708.
Guo Y et al. Study of genes associated with the 'anger-in' and 'anger-out' emotions of humans using a rat model. Exp Ther Med. 2015 Apr;9(4):1448-1454
Hancock DB. Human Genetics of Addiction: New Insights and Future Directions. Curr Psychiatry Rep. 2018: 20, 8.
Hek K et al. A genome-wide association study of depressive symptoms. Biol Psychiatry. 2013:73, 667–78.
Hemby SE. Cocainomics: New Insights into the Molecular Basis of Cocaine Addiction J Neuroimmune Pharmacol. 2010: 5, 70–82.
Hodgson K et al. Genome-wide significant loci for addiction and anxiety. Eur
Psychiatry. 2016: 36, 47-54
Hsuan-Chu S. The Neurobiological Basis of Love: A Meta-Analysis of Human Functional Neuroimaging Studies of Maternal and Passionate Love. .Review Brain Sci. 2022: 12(7):830.
Hyde CL et al. Identification of 15 genetic loci associated with risk of major depression in individuals of European descent. Nat Genet. 2016:48, 1031–6.
Karnib N et al. The Deep Roots of Addiction: A Comparative Perspective. Brain Behav Evol. 2020:95, 222-229
Leonti M, L. Casu. Ethnopharmacology of Love Front Pharmacol 2018: 9, 567.
Long H et al . Structural and functional biomarkers of the insula subregions predict sex differences in aggression subscales. Hum Brain Mapp. 2022 43(9):2923-2935
Marques F et al. (2014) Autism spectrum disorder secondary to enterovirus encephalitis. J. Child Neurol. 29, 708–714.
Mick E et al. Genome-wide association study of proneness to anger. PLoS One 2014 : 9(1):e87257.
Mullins N & Lweis CM. Genetics of Depression: Progress at Last. Curr Psychiatry Rep 2017: 19, 43
Nestler EJ, Psychogenomics: opportunities for understanding addiction. J Neurosci. 2001 : 21, 8324-7
Niarchou M et al. Genome-wide association study of musical beat synchronization demonstrates high polygenicity. Nature Human Behavior 2022: 6, 1292–1309
Okbay A et al. Genetic variants associated with subjective well-being, depressive symptoms, and neuroticism identified through genome-wide analyses. Nat Genet. 2016:48,624–33.
Purcell SM et al. International Schizophrenia Consortium. Common polygenic variation contributes to risk of schizophrenia and bipolar disorder. Nature. 2009;460, 748–52.
Reuter M. The biological basis of anger: associations with the gene coding for DARPP-32 (PPP1R1B) and with amygdala volume. Behav Brain Res 2009: 202(2),179-83
Ripke S et al. Major Depressive Disorder Working Group of the Psychiatric GC, A mega-analysis of genome-wide association studies for major depressive disorder. Mol Psychiatry. 2013;18(4):497–511.
Rujescu D et al. Association of anger-related traits with SNPs in the TPH gene. Mol Psychiatry 2002: 7(9), 1023 – 9.
Sullivan PF et al. Genetic epidemiology of major depression: review and meta-analysis. Am J Psychiatry. 2000;157(10):1552–62.
Zeki S. Minireview -The neurobiology of love. FEBS Letters 581 (2007) 2575–2579