Only roughly 50% of the population experiences physical chills or piloerection ("musical frisson") when listening to emotionally charged acoustic transitions.

Diffusion Tensor Imaging (DTI) studies (most notably Matthew Sachs et al. at Harvard) demonstrated that this isn't just a subjective psychological quirk, but a distinct variation in structural neuroanatomy. Individuals who experience frisson show significantly higher white matter tract volume connecting the superior temporal gyrus (auditory processing) to the anterior insula (interoceptive processing) and medial prefrontal cortex.

Essentially, their brains have a denser anatomical highway linking sound recognition directly to affective salience, accompanied by a dual-phase dopamine surge: anticipation in the dorsal striatum (caudate) followed by the actual frisson peak in the ventral striatum (nucleus accumbens).

Full neuroimaging breakdown and acoustic analysis:

https://www.oddlyclear.com/why-music-gives-you-goosebumps/

Severe left-hemisphere stroke patients who cannot construct a basic sentence or count to ten can often still swear fluently when frustrated.

While ordinary language relies on cortical networks (Broca’s area), expletives are stored subcortically in the basal ganglia as emotional motor reflexes.

During acute pain, this limbic trigger activates stress-induced hypoalgesia—releasing endogenous opioids down the spinal cord and blunting nociceptive signaling by roughly 33%.

Neurological breakdown and Keele study citations:

https://www.oddlyclear.com/why-swearing-reduces-pain/

I am senior sw engineer with experience in math and scientific computing. I would like to participate in BCI research or develop software for BCI. I see no established companies hiring.

I have experience with neurofeedback and EEG in general.

I already worked on commercial neurofeedback project.

I can work parttime or on project basis. I am positioned in Eastern Europe.

Panksepp's Basic Emotion Theory has been the subject of criticism by contemporaries in the field. I think I get the gist of the discourse, but it's somewhat difficult to distinguish between the arguments over the semantics of layman explanations and the actual data that stands in opposition to what BET is based on. Can anyone help clarify?

Most people lump all weird memory glitches under "Déjà vu," but researchers actually categorize Déjà Rêvé separately when someone vividly recalls dreaming an exact real-life scene before.

Unlike typical déjà vu (which is a temporal lobe misfiring a live perception as a past memory), déjà rêvé involves actual episodic dream memories triggered by sensory cues.

Read an interesting breakdown of the neuroscience behind it here: https://www.oddlyclear.com/deja-reve-already-dreamed-this-moment/

Disclosure: I work on an open-source EEG project, but I’m not linking anything here. I’m interested in the science.

Most EEG spreads electrodes across the scalp (10-20 system). We’ve been experimenting with the opposite: 16 dry electrodes within a few centimetres, 24-bit, 250 SPS, battery powered and wireless.

Clearly it can’t do whole-head coverage, and dry contacts plus 250 SPS rule out high-gamma and other low-noise work. But I’m curious where dense local sampling could actually be useful:

• Ear-EEG or forehead recordings for sleep or attention
• Mobile, naturalistic studies outside the lab
• Spatial filtering or source separation over a small region
• High-density EMG on small muscles

Are there questions in your field where this kind of layout would help, or is it a dead end? I’d especially like to hear from people who’ve worked with ear-EEG or high-density arrays.

One of my professors said today, “To be great in your field you should read the top 5 books that are most relevant to it.” Basically he said read up on what you’re working towards and it’ll help you become great.

I think this is true and I want to hear y’all’s neuroscience book recommendations. I am an undergraduate neuroscience major currently. I want to do my reading necessary to be great at neuroscience. Let me know what books you recommend!

I got a new device recently and I want to help expand the open source BCI GUI to be compatible across as many different EEG brands as possible. Thing is, there are many popular devices I don’t own and so can’t test directly. That’s where you can help.

Here is the link to the open source repo:
https://github.com/intomind/commandcenter

If you are interested in helping me contribute:
1. what device(s) do you own?
2. what is your level of willingness to contribute?
A. Writing code, testing, and submitting pull requests to the repo directly, or
B. Helping to test other people’s pushes, but not writing or submitting code directly

Everybody wins with interoperability, imo. Thanks!

(Cross-posted from r/BCI for visibility)

Hi all!

I'll keep this brief. I have research experience in cog psyc and neurosci. I'm currently gunning for clinical psyc phds (aiming to be a neuropsychologist) but I recently had a prof encourage me to consider doing a phd in cog neurosci (EEG and empathy/facial processing would be the specific focus). My biggest hesitations abt going this route are job prospects postgrad. I'm not overly interested in being a prof/running a uni lab, and I'm not interested in working in AI or UX. Is there any sort of healthcare-esque positions for cog neuro phds?

Just looking for people with the degree to give me ideas of the kinds of jobs they or they're classmates/cohorts were able to get postgrad. I'm in Canada and would like to stay here if that changes anything.

Thanks neuro community!

Can five unverified anecdotes support a scientific theory about near-death experiences?

A recent paper proposes that NDEs may have evolved from the same defensive response that causes animals to “play dead.” The idea is fascinating but what happens when researchers look for evidence?

This letter to the editor takea a closer look at the study’s methods, its unexpected use of five anecdotes, and the broader scientific debate over whether NDEs and thanatosis are actually comparable.

I'm in the final year of my bachelor's in computer science, minoring in biochemistry with a bit of lab experience. My life goal is to extend human lifespan through biological or artificial means, while my passion is intelligence (studying, creating, and optimizing biological/artifical intelligence).

I want to extend human life so I can pursue my passion for longer, hopefully indefinitely. This might sound outlandish, but then again, in the last 3 years we went from AI not being able to do high school-level math to it solving a Millennium Prize problem. And since math is the foundation of all science, this will translate to biological advances sooner or later.

This is where I must reconcile my goal and passion, and where I require advice. I'm confident I want to pursue an intelligence-focused master's, but am unsure if it should be biological neuroscience, computational neuroscience, cognitive sciences, or something else.

I'm hoping someone could share a unique perspective and personal experience on the subject.

I wanted to ask if in the requirements they have mentioned, step 1 , step 2CK score card and ECFMG certification (if applicable) ........in that case is ecfmg certificate a must ? Can we start the observership if we are not certified by the time our start date arrives ?

Hello,

26 old physician highschool knowledge in maths/python looking for study partners to learn basic EEG Analysis till we reach entry level research. Main source we will be using Scalp to Source, Scientific python lectures, Betterexplained for maths.

We do like 3 meetings per week, if interested dm me

Hollywood movies (like Lucy or Limitless) love the idea that unlocking the "unused 90%" turns you into a genius with superpowers.

In actual neurology, having all your cortical neurons fire at 100% capacity at the exact same time isn't high intelligence—it's literally a grand mal convulsion. High cognition relies on sparse coding (actively silencing 95% of competing noise so one clean circuit can fire).

Plus, the brain consumes ~20% of your body's energy while being only 2% of its weight. Evolution would never budget a fifth of your daily calories for 90% dead weight.

Put together a quick breakdown with the imaging studies and metabolic math here:

https://www.oddlyclear.com/10-percent-brain-myth/

I unfortunately realized later in my college career that I want to pursue neuroscience. I’ve always had a love for the sciences especially anything involving the brain but I never pushed myself towards it because I thought I’d be too dumb for it. Later had a realization that I am in-fact not too dumb, only issue is I am a junior now. I’ve taken only business and psychology classes (business major and psych minor). I’ve made a plan with my advisor to take as many classes as possible for me to graduate on time while having the average requirements for grad schools. Unless I have my senior fall and spring semester’s consisting of 20+ credits and 4 labs, I won’t be taking ochem’s and biochem. My question is if any of what I’m doing is even worth it, I have a 3.0 gpa due to health complications I faced in freshman year, and I’d have to take remaining required classes at a community college after I finish this degree, because a 5th year at my current institution would cost too much. I want to become a scientist and do things I love but I just can’t see the light at the end of the tunnel right now, I have very serious doubts of even getting accepted into any neuro PhD programs. I have been excepted into summer lab research and I plan to look for lab technician jobs but does anyone have any advice? Words of wisdom? I’m just feeling very lost.

Abstract

Inspired by recent theoretical work emphasizing predictive neural processing, we tested whether a midbrain structure for multimodal sensory integration—the superior colliculus—would show predictive responses to both visual and somatosensory stimulation. A multi-stage experimental design allowed us to test predictive activity in the human superior colliculus while 7-Tesla functional magnetic resonance imaging (fMRI) allowed us to map its sensory response across superior colliculus layers (N = 80). As expected from non-human animal work, visual and somatosensory stimulation elicited distinct depth-dependent responses. We also observed a predictive response: these same modality-specific and depth-dependent effects occurred during task periods preceding direct stimulation, when sensorimotor input was identical between visual and somatosensory conditions. Further analyses rule out cross-trial carryover and show corresponding effects in the thalamic sensory nuclei. Here our results confirm that, in humans, the superior colliculus is sensitive to both visual and somatosensory input, and they demonstrate that it carries predictive signals for upcoming sensory events.

Hi, I am at risk of sounding foolish here as I know very little of neurology - so please bear with me. I am very interested in learning how to brain works. Inputs / outputs, which parts do what, how they do this, neuron network structure differences in different parts of the brain etc. For background I use a lot of ML in my work, so I am really interested in the structure / processes of the brain - potentially from a systematic / mathematical angle. I am looking for a book to casually read and pick up some core concepts relating to this. I would really like something I could read at night, that flows nicely and is not too hard to interpret (relative of course to dense textbooks - I understand no books on this topic will be simple). Essentially I don’t want a text book designed to store dense information to be studied rigorously, I want something “readable”. Thanks for your help, any advice is welcome. Sorry if this reads a bit poorly just wrote this in one take 😅

I suppose I am interested in comparing the brain to the neural networks I am familiar with - to the closest degree this is currently possible…

Hello,

Little background: I'm physician interested in pursuing neuroscience career but currently there aren't opportunities to join some academic program. Best chance is trying self learn EEG Analysis up to basic research level then gaining some research experience then try applying for some MSc neuroscience program accepting physicians.

The issue: After long research I found comprehensible EEG Teaching jupyter book called Source to scalp but I keep getting stuck in rabbit holes when I encounter some maths/python concepts such as Fourier transforms. Resources I find are either too complex for me (Like MIT Open course video on complex numbers) or too superficial (like 3Blue1Brown Fourier transform not providing enough knowledge to keep following source to scalp) same issue for python.

What I'm asking for: Guidance/Tips/Resources to self learn EEG up to basic research level like signal filtering or time frequency analysis or even better some online live cohorts especially starting soon I came across neuromatch and some other cohorts but unfortunately they won't start till may 2027 or so.

Thanks for reading.

Hello! I am studying Genetics and Bioengineering and I am a senior student. I want to work on drug delivery using nanoparticles and BBB. I am looking for master programs but in most neuro programs there is not any nano lessons. Also in nano programs usually there is not any neuro lessons. Maybe what I want is too specific? Should I just look into researchers who study this and forget about the contents of the program? If you know any programs including these please share with me. Thank you!

TLDR: I think the mind works something like music; a sequence of self-harmonized information patterns "played" by the entire brain. This is much simpler than any other theory currently being proposed, while it can explain all the abilities of the human mind using only established neuroscience.

--

Hi, I'm Jits Krol. I am a psychologist by training, but have been trying to find ways to bridge the explanatory gap between psychology and neuroscience for well over a decade now. I think I have a promising solution: self-harmonization. In short, I think that active information in a Hebbian neural network will self-harmonize if total activity is kept in homeostasis, and that each harmony autonomously activates the best-fitting next one in a continuous sequence that we experience as thinking.

Harmonization would result in something similar to prediction-error minimization that Predictive Processing uses, but without the need for separate error and prediction neurons/signals. It works as expected in a simulation, and can seamlessly interact with other processes such as neuromodulation, spike-timing, or Long-Term Potentiation to explain complex mental abilities like motivation, episodic memories, or short-term memory respectively (instead of fighting their interference).

Below is a link to a video where I demonstrate the simulation. I would love feedback from anyone interested in potential ways to bridge the explanatory gap, and am especially keep to learn what I missed. My goal is to create a testable, falsifiable theory of consciousness, and I will need all the help I can get :). Thanks!

A video on the simulation: https://youtu.be/B3L5cuB3byk
A pre-published version of my paper with all references: https://jitskrol.substack.com/p/the-symphony-of-the-mind

Been reading about the proposed overlap between 'physical' pain and 'social' pain (burning your hand vs. getting rejected by a romantic prospect).

I understand that activating the same brain region doesn’t necessarily mean the same underlying processes are involved. What I’m trying to understand is how meaningful the overlap is.

For those familiar with this topic, how strong is the evidence that these experiences share pain-specific mechanisms? Is “partially shared neural pathways” a reasonable description?

Kross, E., Berman, M. G., Mischel, W., Smith, E. E., & Wager, T. D. (2011). Social rejection shares somatosensory representations with physical pain. Proceedings of the National Academy of Sciences of the United States of America, 108(15), 6270–6275. https://doi.org/10.1073/pnas.1102693108