Most accurate depiction of cortical homunculus?

Most accurate depiction of cortical homunculus?

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I was looking at cortical homunculus and I realized there are several different pictures and they don't quite agree. For instance:

One area, for instance, are the feet, which I feel should have a fairly large (not as big as hands) area in the brain, mainly from the personal experience of having more sensations in feet, which like the hands has many nerve endings, though obviously much less fine motor ability. But that's just my speculation, and I like an accurate depiction so I can start from there and make sense of this. Thank you.

Be sure to distinguish exactly what the diagram is showing. Your first reference is sensory cortex mapping. The second reference is motor cortex mapping. The text of the second reference is admittedly confusing about this, a search for "Wilder Penfield motor cortex" verifies.

It seems reasonable the feet would show sensory response similar to the hands. Stepping on a nail is just about as bad as bumping your hand into one. Motor control is different. Muscles are not all innervated the same. In some of the large muscles, say in the legs, one nerve can control hundreds of cells. In other cases, particularily in the face, one neuron might control only six muscle cells. Thus a greater number of neurons, and a larger portion of the brain, is needed for control in these delicately controlled areas. It would make sense a greater part of our brain would be needed for motor control in our hands versus our feet.

In vivo imaging of injured cortical axons reveals a rapid onset form of Wallerian degeneration

Despite the widespread occurrence of axon and synaptic loss in the injured and diseased nervous system, the cellular and molecular mechanisms of these key degenerative processes remain incompletely understood. Wallerian degeneration (WD) is a tightly regulated form of axon loss after injury, which has been intensively studied in large myelinated fibre tracts of the spinal cord, optic nerve and peripheral nervous system (PNS). Fewer studies, however, have focused on WD in the complex neuronal circuits of the mammalian brain, and these were mainly based on conventional endpoint histological methods. Post-mortem analysis, however, cannot capture the exact sequence of events nor can it evaluate the influence of elaborated arborisation and synaptic architecture on the degeneration process, due to the non-synchronous and variable nature of WD across individual axons.


To gain a comprehensive picture of the spatiotemporal dynamics and synaptic mechanisms of WD in the nervous system, we identify the factors that regulate WD within the mouse cerebral cortex. We combined single-axon-resolution multiphoton imaging with laser microsurgery through a cranial window and a fluorescent membrane reporter. Longitudinal imaging of > 150 individually injured excitatory cortical axons revealed a threshold length below which injured axons consistently underwent a rapid-onset form of WD (roWD). roWD started on average 20 times earlier and was executed 3 times slower than WD described in other regions of the nervous system. Cortical axon WD and roWD were dependent on synaptic density, but independent of axon complexity. Finally, pharmacological and genetic manipulations showed that a nicotinamide adenine dinucleotide (NAD + )-dependent pathway could delay cortical roWD independent of transcription in the damaged neurons, demonstrating further conservation of the molecular mechanisms controlling WD in different areas of the mammalian nervous system.


Our data illustrate how in vivo time-lapse imaging can provide new insights into the spatiotemporal dynamics and synaptic mechanisms of axon loss and assess therapeutic interventions in the injured mammalian brain.

How Going Barefoot Affects Your Brain | By Dr. Sam Oltman, ND

The anatomical, structural and functional benefits of going barefoot and wearing naturally-shaped shoes are often the emphasis of our education because these tend to be the most immediate and practical. There is, however, another massive benefit to being barefoot and it lies not in your feet but in your head—your brain, to be exact.

The brain, just like muscles, bones and connective tissue operates on the “use it or lose it” principle. Meaning, if an area of the brain is not stimulated it will atrophy, weaken and shrink. Conversely, if an area of the brain is stimulated regularly and used routinely, it can grow in both size and in the number of neuronal connections. This is the basis for the phenomenon of ‘neuroplasticity,” which is the ability of the brain to change and grow throughout adulthood.

To understand the benefits going barefoot has on the brain, we need to understand the “homunculus.” Homunculus is Latin for “little person” and in the context of biology, is the word used to describe the brain’s model for the body. There are two divisions of the homunculus—the sensory and the motor. The sensory homunculus is the area of the brain where the body is mapped out in proportion to the density of sensory neurons that correspond to various parts of the body. The motor homunculus is the area where the map is based on motor function. Areas of the body that have a higher density of neurons for either sensory or motor function take up a more extensive section of the brain (which is why the pictorial depiction of the homunculus is so strange-looking). When we use a particular body part more routinely for either feeling or moving, the homunculus in the corresponding area of the brain is stimulated and becomes more developed. With more use, a higher resolution map of that particular area forms in the brain.

Homonculus Sensory and Motor Cortex. Digital Image. Evidence-Based Medicine Consult. Web. 29 January 2019. <>

When we are barefoot, we receive a massive amount of sensory feedback from our feet—far more than when we are in overly-supportive and overly-cushioned shoes. We receive increased information from the foot about its position in space, the texture of the ground and muscle tension. Being barefoot sharpens the homunculus of the foot in the brain and lays the foundation for better balance and improved motor control through the increased information intake and subsequent brain growth. When someone wears overly supportive and cushioned shoes, the sensory homunculus becomes underdeveloped, and the information the brain receives from the foot is distorted, resulting in a lack of control. When someone is barefoot regularly, the sensory feedback from the foot becomes more detailed and refined, allowing the foot and brain to delineate small changes of sensory stimuli. The result is better control of motor function and balance. This is especially important as we age because the loss of balance is the top reason for falls in the elderly.

The increase in sensory detail while barefoot plays out practically in several ways. First, it allows for increasingly refined motor control based on sensory feedback. For example, when running barefoot or in naturally shaped shoes you can accurately adjust how your foot strikes by a matter of millimeters. In contrast, running in a conventional shoe there is little feedback, and making micro-adjustments in gait is not only difficult but discouraged by the design of the shoe. Second, by using and stimulating the nerves in the foot more, their physical growth is encouraged (both in the peripheral nerves of the foot and the central neurons in the brain). This growth can have beneficial impacts on circulation and sensitivity. Finally, there is pure enjoyment in being able to feel and connect with various textures and surfaces. Being barefoot on the beach, in the grass or on the rocks of a riverbed is immensely pleasurable and provides additional physiological benefits too.

Along with the increased muscular strength, the enhanced circulatory flow and improved anatomical alignment, going barefoot has very tangible benefits on the brain and the nervous system. This results in improved balance, better motor control and more enjoyment. Add the changes in brain function to the long list of reasons to consider ditching constrictive shoes and allow your feet (and brain) to feel more.

Somatosensory plasticity and localization

After stimulating the skin surface, activity travels through the thalamus to primary somatosensory cortex (S1). The somatosensory cortex is organized topographically, such that (with a few exceptions) adjacent locations of the body are represented in neighboring locations on the map (see figure 3). Although it is topographic, the relationship between the size of the skin surface and the size of the map is not uniform across all regions of the body. These non-uniformities can arise through regional differences in the density of sensory innervation or limb usage.

Somatosensory cortex is also plastic. In non-human primates, the reorganization of the somatosensory cortex after amputation, skin island transfers, and other interventions are well studied (Merzenich & Jenkins, 1993). For example, when the third digit is removed by amputation the representations of the palm and adjacent digits expand into this space, so that the second and fourth digits now share a border in the cortex. Intensive stimulation of the skin surface also results in cortical reorganization. The spatial and temporal properties of the stimulation determine how S1 is reorganized. When the fingers of monkeys are stimulated simultaneously for a prolonged period, the finger representations become closer, whereas sequential stimulation moves them apart (Wang et al. 1995). In humans, synchronous stimulation of the fingers also results in changes in S1. Braun and colleagues (Braun et al. 2000) touched participants simultaneously on the first and fifth digits for an hour a day until twenty hours was reached. Near-threshold touch of either finger was misattributed to the other finger at a much higher rate than measured before the experiment. Thus increased usage not only induces topographic changes in S1, but also changes the perceived location of touch. Touch arising from self-generated movement can have similar effects experienced piano players have much better two-point discrimination thresholds on the fingertips compared to non-musicians (Ragert et al. 2004). In addition their tactile acuity on the fingers has a dose-dependent relationship with hours of practice.

Studies of cortical plasticity demonstrate extensive changes in S1 topography. However, less research has been done examining how potential changes in S1 topography, due to plasticity, relate to changes in perception. Given that S1 is plastic, the relationship between activity in a specific region of S1 and perceiving touch in a particular location on the skin surface cannot be fixed, such that one set of neurons always represents touch at a specific location. There must be further processing that takes information from somatosensory regions and interprets it, such that conscious perception of touch location emerges. Very little is known about exactly how the brain interprets somatosensory activity as a particular tactile sensation. Some initial evidence towards understanding this comes from individuals with brain damage due to stroke.

Individuals who have had strokes in somatosensory regions often report reduced sensitivity to touch along with biases in tactile localization. For example, stroke patients often demonstrate localization errors such that tactile stimuli are localized towards the center of the hand (Rapp, Hendel & Medina, 2002). Interestingly, healthy individuals show similar “central” biases when presented with near-threshold tactile stimuli. For example, weaker touch on the forearm is mislocalized toward its middle (Steenbergen et al. 2014). Why would individuals with somatosensory damage presented with suprathreshold stimuli, along with neurologically intact individuals with near-threshold stimuli, demonstrate such a central tendency? General models that explain spatial bias under uncertainty could explain these tactile localization biases. Huttenlocher and colleagues proposed the category adjustment model (Huttenlocher & Others, 1991) to explain biases in spatial memory. Memories of spatial locations are biased towards the middle of a categorical space, and away from category boundaries, resulting in central error. Importantly, this central error increases as a function of uncertainty. In both cases (suprathreshold touch for brain-damaged individuals, and near-threshold touch for neurologically-intact individuals), somatosensory information is noisy and uncertain. One possibility is that, in interpreting information from somatosensory regions, the brain uses similar heuristics to interpret this noisy activation as touch in a particular location.

Somatosensory System

We use touch to interpret texture, shape, size, weight, perform tasks and use tools.

Smallest receptive fields

Endings surrounded by Schwann cell capsule

High sensitivity but larger receptive fields than Merkel cells and lower spatial resolution

Light touch, Vibration, slipping grip

Endings surrounded by membrane layers (onion)

High sensitivity, huge receptive field

Vibrations through tools, pressure

Oriented with stretch lines

Sensitive to stretching during movement

Type 1 Αδ fibers- dangerous mechanical or chemical

Respond to mechanical, chemical and heat

Difficult to treat with analgesics/NSAIDs because not "chemical soup" mediated

Pain afferents from the viscera (organs such as the heart) enter the spinal cord at the same DRG as pain fibers from the skin

Visceral afferents synapse with the same second order neurons in the spinal cord as the pain afferents from skin

Results in confusion in the interpretation of source of pain

This confusion causes the brain to interpret visceral pain as cutaneous pain

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Structure of the human musculoskeletal network

To examine the structural interconnections of the human musculoskeletal system, we used a hypergraph approach. Drawing from recent advances in network science [5], we examined the musculoskeletal system as a network in which bones (network nodes) are connected to one another by muscles (network hyperedges). A hyperedge is an object that connects multiple nodes muscles link multiple bones via origin and insertion points. The degree, k, of a hyperedge is equal to the number of nodes it connects thus, the degree of a muscle is the number of bones it contacts. For instance, the trapezius is a high-degree hyperedge that links 25 bones throughout the shoulder blade and spine conversely, the adductor pollicis is a low-degree hyperedge that links 7 bones in the hand (Fig 2a and 2b). A collection of hyperedges (muscles) that share nodes (bones) is referred to as a hypergraph: a graph H = (V, E) with N nodes and M hyperedges, where V = <>1,···, vN> is the set of nodes and E = <>1,···, eM> is the set of hyperedges.

(a) Left: Anatomical drawing highlighting the trapezius. Right: Transformation of the trapezius into a hyperedge (red degree k = 25), linking 25 nodes (bones) across the head, shoulder, and spine. (b) Adductor pollicis muscle linking 7 bones in the hand. (c) Spatial projection of the hyperedge degree distribution onto the human body. High-degree hyperedges are most heavily concentrated at the core. (d) The musculoskeletal network displayed as a bipartite matrix (1 = connected, 0 otherwise). (e) The hyperedge degree distribution for the musculoskeletal hypergraph, which is significantly different than that expected in a random hypergraph. Data available for (e) at DOI:10.5281/zenodo.1069104.

The representation of the human musculoskeletal system as a hypergraph facilitates a quantitative assessment of its structure (Fig 2c). We observed that the distribution of hyperedge degree is heavy-tailed: most muscles link 2 bones, and a few muscles link many bones (Fig 2d and 2e). The skew of the degree distribution differs significantly from that of random networks (two-sample Kolmogorov-Smirnov test, KS = 0.37, p < 0.0001, see Materials and methods) [5], indicating the presence of muscles of unexpectedly low and high degree (Fig 2e).

Function of the human musculoskeletal network

To probe the functional role of muscles within the musculoskeletal network, we employed a simplified model of the musculoskeletal system and probed whether the model could generate useful clinical correlates. We implemented a physical model in which bones form the core scaffolding of the body, while muscles fasten this structure together. Each node (bone) is represented as a mass, whose spatial location and movement are physically constrained by the hyperedges (muscles) to which it is connected. Specifically, bones are points located at their center of mass, derived from anatomy texts [19], and muscles are springs (damped harmonic oscillators) connecting these points [40,41] for a hyperedge of degree k, we created k(k − 1)/2 springs linking the k nodes. That is, for a muscle connecting k bones, we placed springs such that each of the k muscles had a direct spring connection to each of the other k − 1 bones.

Next, we perturbed each of 270 muscles in the body and calculated their impact score on the network (see Materials and methods and Fig 1c and 1d). As a muscle is physically displaced, it causes a rippling displacement of other muscles throughout the network. The impact score of a muscle is the mean displacement of all bones (and indirectly, muscles) resulting from its initial displacement. We observed a significant positive correlation between muscle degree and impact score (F(1,268) = 23.3, R 2 = 0.45, p < 0.00001 Fig 3a), suggesting that hyperedge structure dictates the functional role of muscles in the musculoskeletal network. Muscles with a larger number of insertion and origin points have a greater impact on the musculoskeletal system when perturbed than muscles with few insertion and origin points [42]. We can gain further insights into the results of these analyses by explicitly studying the relation between impact score and statistical measures of the network’s topology. In S11 Fig, we show that the network function as measured by the impact score was significantly correlated with the average shortest path length. While the network statistics are static in nature, their functional interpretation is provided by the perturbative simulations of system dynamics.

(a) The impact score plotted as a function of the hyperedge degree for a null hypergraph model and the observed musculoskeletal hypergraph. (b) Impact score deviation correlates with muscle recovery time following injury to muscles or muscle groups (F(1,12) = 37.3, R 2 = 0.757, p < 0.0001). Shaded areas indicate 95% confidence intervals, and data points are scaled according to the number of muscles included. The plot is numbered as follows, corresponding to Table 4: triceps (1), thumb (2), latissimus dorsi (3), biceps brachii (4), ankle (5), neck (6), jaw (7), shoulder (8), teres major (9), hip (10), eye muscles (11), knee (12), elbow (13), wrist/hand (14). Data available at DOI:10.5281/zenodo.1069104.

To guide interpretation, it is critical to note that the impact score, while significantly correlated with muscle degree, is not perfectly predicted by it (Fig 3a). Instead, the local network structure surrounding a muscle also plays an important role in its functional impact and ability to recover. To better quantify the effect of this local network structure, we asked whether muscles existed that had significantly higher or significantly lower impact scores than expected in a null network. We defined a positive (negative) impact score deviation that measures the degree to which muscles are more (less) impactful than expected in a network null model (see Materials and methods). This calculation resulted in a metric that expresses the impact of a particular muscle, relative to muscles of identical hyperedge degree in the null model. In other words, this metric accounts for the complexity of a particular muscle (Table 1).

Is this mathematical model clinically relevant? Does the body respond differently to injuries to muscles with higher impact score than to muscles with lower impact score? To answer this question, we assessed the potential relationship between muscle impact and recovery time following injury. Specifically, we gathered data on athletic sports injuries and the time between the initial injury and return to sport. Critically, we observed that recovery times were strongly correlated with impact score deviations of the individual muscle or muscle group injured (F(1,12) = 37.3, R 2 = 0.757, p < 0.0001 Fig 3b), suggesting that our mathematical model offers a useful clinical biomarker for the network’s response to damage. We note that it is important to consider the fact that recovery might be slower in a person who is requiring maximal effort in a performance sport, compared to an individual who is seeking only to function in day-to-day life. In order to generalize our findings to the entire population, we therefore also examined recovery time data collected from nonathletes, and we present these complementary results in the Supporting information (S6 Text).

Finally, to provide intuition regarding how focal injury can produce distant effects potentially slowing recovery, we calculated the impact of the ankle muscles and determined which other muscles were most impacted. That is, for each individual ankle muscle, we calculated the impact on each of the remaining 264 non-ankle muscles and then averaged this over all ankle muscles. Out of the 264 non-ankle muscles, the single muscle that is most impacted by the perturbation of ankle muscles is the biceps femoris of the hip, and the second most impacted is the vastus lateralis of the knee. Additionally, the muscle most impacted by perturbation to hip muscles is the soleus.

Control of the human musculoskeletal network

What is the relationship between the functional impact of a muscle on the body and the neural architecture that affects control? Here, we interrogate the relationship between the musculoskeletal system and the primary motor cortex. We examined the cerebral cortical representation map area devoted to muscles with low versus high impact by drawing on the anatomy of the motor strip represented in the motor homunculus [43] (Fig 4a), a coarse one-dimensional representation of the body in the brain [44]. We observed that homunculus areas differentially control muscles with positive versus negative impact deviation scores (Table 2). Moreover, we found that homunculus areas controlling only positively (negatively) deviating muscles tend to be located medially (laterally) on the motor strip, suggesting the presence of a topological organization of a muscle’s expected impact in neural tissue. To probe this pattern more deeply, for each homunculus area, we calculated a deviation ratio as the percent of muscles that positively deviated from the expected impact score (i.e., a value of 1 for brow, eye, face and a value of 0 for knee, hip, shoulder see Table 2). We found that the deviation ratio was significantly correlated with the topological location on the motor strip (F(1,19) = 21.3, R 2 = 0.52, p < 0.001 Fig 4b).

(a) The primary motor cortex homunculus as constructed by Penfield. (b) Deviation ratio correlates significantly with homuncular topology (F(1,19) = 21.3, R 2 = 0.52, p < 0.001), decreasing from medial (area 0) to lateral (area 22). (c) Impact score deviation significantly correlates with motor strip activation volume (F(1,5) = 14.4, R 2 = 0.743, p = 0.012). Data points are sized according to the number of muscles required for the particular movement. The plot is numbered as follows, corresponding to Table 5: thumb (1), index finger (2), middle finger (3), hand (4), all fingers (5), wrist (6), elbow (7). (d) Correlation between the spatial ordering of Penfield’s homunculus categories and the linear muscle coordinate from a multidimensional scaling analysis (F(1,268) = 316, R 2 = 0.54, p < 0.0001). Data available at DOI:10.5281/zenodo.1069104.

As a stricter test of this relationship between a muscle’s impact on the network and neural architecture, we collated data for the physical volumes of functional MRI-based activation on the motor strip that are devoted to individual movements (e.g., finger flexion or eye blinks). Activation volumes are defined as voxels that become activated (defined by blood-oxygen-level-dependent signal) during movement [38,39]. Critically, we found that the functional activation volume independently predicts the impact score deviation of muscles (Fig 4c, F(1,5) = 14.4, p = 0.012, R 2 = 0.743), consistent with the intuition that the brain would devote more real estate in gray matter to the control of muscles that are more impactful than expected in a null model. Again, impact deviation is a metric that accounts for the hyperedge degree of a particular muscle and is relative to the impact of muscles with identical hyperedge degree in the null model. Thus, the impact deviation measures the local network topology beyond simply the immediate connections of the muscle in question.

As a final test of this relationship, we asked whether the neural control strategy embodied by the motor strip is optimally mapped to muscle groups. We constructed a muscle-centric graph by connecting two muscles if they touch on the same bone (Fig 1c, left). We observed the presence of groups of muscles that were densely interconnected with one another, sharing common bones. We extracted these groups using a clustering technique designed for networks [45,46], which provides a data-driven partition of muscles into communities (Fig 1b, right). To compare the community structure present in the muscle network to the architecture of the neural control system, we considered each of the 22 categories in the motor homunculus [18] as a distinct neural community and compared these brain-based community assignments with the community assignments obtained from a data-driven partition of the muscle network. Using the Rand coefficient [47], we found that the community assignments from both homunculus and muscle network were statistically similar (zRand > 10), indicating a correspondence between the modular organization of the musculoskeletal system and the structure of the homunculus. For example, the triceps brachii and the biceps brachii belong to the same homuncular category, and we found that they also belong to the same topological muscle network community.

Next, because the homunculus has a linear topological organization, we asked whether the order of communities within the homunculus (Table 3) was similar to a data-driven ordering of the muscle groups in the body, as determined by MDS [48]. From the muscle-centric network (Fig 1b), we derived a distance matrix that encodes the smallest number of bones that must be traversed to travel from one muscle to another. An MDS of this distance matrix revealed a one-dimensional linear coordinate for each muscle, such that topologically close muscles were close together and topologically distant muscles were far apart. We observed that each muscle’s linear coordinate is significantly correlated with its homunculus category (Fig 4d, F(1,268) = 316, p < 0.0001, R 2 = 0.54), indicating an efficient mapping between the neural representation of the muscle system and the network topology of the muscle system in the body.

Our results from Fig 4d demonstrate a correspondence between the topology of the homunculus and a data-driven ordering of muscles obtained by considering the topological distances between them. This result could be interpreted in one of two ways: one reasonable hypothesis is that because most connections in the musculoskeletal network are short range, the finding is primarily driven by short-range connections. A second reasonable hypothesis is that while short-range connections are the most prevalent, long-range connections form important intramodular links that help determine the organization of the network. To arbitrate between these two hypotheses, we considered two variations of our MDS experiment: one including only connections shorter than the mean connection length and the other including only connections longer than the mean connection length. We found that the data-driven ordering derived from only short and only long connections both led to significant correlations with the homuncular topology (F(1,268) = 24.9, R 2 = 0.085, p < 0.0001 and F(1,268) = 5, R 2 = 0.018, p = 0.026, respectively). Notably, including both long and short connections leads to a stronger correlation with homuncular topology than considering either independently, suggesting a dependence on connections of all lengths. It would be interesting in the future to test the degree to which this network-to-network map is altered in individuals with motor deficits or changes following stroke.

What Did We Learn from the Molecular Biology of Adrenal Cortical Neoplasia? From Histopathology to Translational Genomics

Approximately one-tenth of the general population exhibit adrenal cortical nodules, and the incidence has increased. Afflicted patients display a multifaceted symptomatology—sometimes with rather spectacular features. Given the general infrequency as well as the specific clinical, histological, and molecular considerations characterizing these lesions, adrenal cortical tumors should be investigated by endocrine pathologists in high-volume tertiary centers. Even so, to distinguish specific forms of benign adrenal cortical lesions as well as to pinpoint malignant cases with the highest risk of poor outcome is often challenging using conventional histology alone, and molecular genetics and translational biomarkers are therefore gaining increased attention as a possible discriminator in this context. In general, our understanding of adrenal cortical tumorigenesis has increased tremendously the last decade, not least due to the development of next-generation sequencing techniques. Comprehensive analyses have helped establish the link between benign aldosterone-producing adrenal cortical proliferations and ion channel mutations, as well as mutations in the protein kinase A (PKA) signaling pathway coupled to cortisol-producing adrenal cortical lesions. Moreover, molecular classifications of adrenal cortical tumors have facilitated the distinction of benign from malignant forms, as well as the prognostication of the individual patients with verified adrenal cortical carcinoma, enabling high-resolution diagnostics that is not entirely possible by histology alone. Therefore, combinations of histology, immunohistochemistry, and next-generation multi-omic analyses are all needed in an integrated fashion to properly distinguish malignancy in some cases. Despite significant progress made in the field, current clinical and pathological challenges include the preoperative distinction of non-metastatic low-grade adrenal cortical carcinoma confined to the adrenal gland, adoption of individualized therapeutic algorithms aligned with molecular and histopathologic risk stratification tools, and histological confirmation of functional adrenal cortical disease in the context of multifocal adrenal cortical proliferations. We herein review the histological, genetic, and epigenetic landscapes of benign and malignant adrenal cortical neoplasia from a modern surgical endocrine pathology perspective and highlight key mechanisms of value for diagnostic and prognostic purposes.

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Why do we feel more details when we touch something with the tips of our finger, than the rest of our body?

Why do we feel more details when we touch something with the tips of our finger, than the rest of our body?

Is this because we have different kind of nerves (sensors) on the tips of our fingers than the rest of our body? Or is it our brain thats somehow causing this to happen?

Your fingers are made to touch! It's useful to be able to get very detailed tactile sensations. Your fingertips have a higher concentration of mechanoreceptors. A popular high school experiment is to place a blindfold on someone and pick a part of their body with two pencils varying distances apart. You'll find that you can distinguish there are two separate points of contact if you do it on your hand, while your back will require them to be much father apart before you can sense two points of contact!

Doctor here - yes, there is a much higher density of nerves on your fingertips, but they also map (nerve for nerve) to a larger area of the sensory cortex of your brain. You can see a visual depiction of the area dedicated to each region of the body in your brain by looking at the Sensory Homunculus. One of the many tests we do to check that your sensory cortex and peripheral nerves are all intact when we're worried about it is the high school experiment above - we call it Two Point Discrimination.

The main reason is that the density of mechanoreceptor cells is higher in your fingertips than other parts of the body. You can see that by measuring the minimum distance where you can tell the difference between two points and one point touching your skin, as summarized in figure 9.4 here.

Detecting Prejudice In The Brain

Three Florida teenagers recently pleaded not guilty to the brutal beatings and in one case, death, of homeless men. One of the beatings was caught on surveillance video and in a most chilling way illustrates how people can degrade socially outcast individuals, enough to engage in mockery, physical abuse, and even murder. According to new research, the brain processes social outsiders as less than human brain imaging provides accurate depictions of this prejudice at an unconscious level.

A new study by Princeton University psychology researchers Lasana Harris and Susan Fiske shows that when viewing photographs of social out-groups, people respond to them with disgust, not a feeling of fellow humanity. The findings are reported in the article "Dehumanizing the Lowest of the Low: Neuro-imaging responses to Extreme Outgroups" in a forthcoming issue of Psychological Science, a journal of the Association for Psychological Science (previously the American Psychological Society).

Twenty four Princeton University undergraduates viewed a large number of color photographs of different social groups (including Olympic athletes, business professionals, elderly people, and drug addicts), and images of objects (including the Space Shuttle, a sports car, a cemetery, and an overflowing toilet) that elicited the emotions of pride, envy, pity, or disgust. The four emotions were derived from the Stereotype Content Model (SCM), which predicts differentiated prejudices based on warmth and competence. Warmth was determined by friendliness, competence by capability. The two emotional extremes were pride and disgust pride elicited high warmth and high perception of competence, and disgust elicited low warmth and low perception of competence. Envy and pity were considered moderate prejudices envy elicited low warmth and high perception of competence, and pity elicited high warmth and low perception of competence.

Medial prefrontal cortex (MPFC) brain imaging determined if the students accurately chose the correct emotion illustrated by the picture (according to pretest results in which a different group of students determined the emotion that best fit each photograph). The MPFC is only activated when a person thinks about him- or her-self or another human. When viewing a picture representing disgust, however, no significant MPFC brain activity was recorded, showing that students did not perceive members of social out-groups as human. The area was only activated when viewing photographs that elicited pride, envy, and pity. (However, other brain regions -- the amygdala and insula -- were activated when viewing photographs of "disgusting" people and nonhuman objects.)

Emotions themselves were not responsible for generating this brain activity. Rather, it was the actual image viewed that produced a response. The MPFC only showed significant activity when a person saw or thought about a human being. The authors conclude that this lack of MPFC brain activity while viewing photographs of people proves that "members of some social groups seem to be dehumanized."

Social out-groups are perceived as unable to experience complex human emotions, share in-group beliefs, or act according to societal norms, moral rules, and values. The authors describe this as "extreme discrimination revealing the worst kind of prejudice: excluding out-groups from full humanity." Their study provides evidence that while individuals may consciously see members of social out-groups as people, the brain processes social out-groups as something less than human, whether we are aware of it or not. According to the authors, brain imaging provides a more accurate depiction of this prejudice than the verbal reporting usually used in research studies.

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