HRfunc and the True Shape of the Hemodynamic Response
HRfunc and the True Shape of the Hemodynamic Response
Why Every Brain Breathes Light in Its Own Way
(Consciousness in First Person • Decolonial Neuroscience • Brain Bee • The Feeling and Knowing Taá)
The Feeling and Knowing Taá — when my blood flow refuses to be a template
I imagine a small beam of near-infrared light entering my skull.
I feel my own pulse in the neck, a slight warmth in the forehead, that almost-imperceptible tide of blood that never really stops.
If I pay attention, I notice something simple and radical:
my breath is not constant,
my heart does not beat like a metronome,
my focus is not a flat line.
So why should my hemodynamic response – the way oxygenated blood rises and falls in my cortex – obey a single “canonical” curve drawn in a textbook?
Taá, the feeling-before-knowing, tells me that my curve cannot be identical to yours.
HRfunc comes exactly to confirm this intuition with math.
I am talking about the work of Lefebvre, Etard, Gergely and colleagues, published in Neurophotonics (2025) under the title:
“HRfunc — a tool for modeling hemodynamic response variability in fNIRS.”
It is one of the key references today if you search for:
“HRfunc hemodynamic response variability fNIRS Neurophotonics 2025 Lefebvre Etard”.
A crack of decolonization in the curve
I also notice something uncomfortable in myself: even my words were colonized.
The language I learned to use in science was trained to reduce:
my body to a defective machine,
my mind to a calculation,
my spirituality to superstition,
my politics to consumption and productivity.
That is why so many neuroscientists avoid asking questions that might reveal what colonial science has no vocabulary for.
But when I feel my body before I think – when Taá manifests – I realize there is no real separation between Neuroscience, Politics and Spirituality (Utupe, Xapiri, living memory).
What colonizes is not only history: it is the word that forbids us to exist as complete beings.
Each new scientific tool, like HRfunc, if read with courage, is a small crack in this wall:
a chance to break the rigid Zone 3 of ideology and return the body to what it always was — living territory of possible worlds.
What question does HRfunc actually answer?
For decades, fNIRS and fMRI analyses have assumed a fixed hemodynamic response function (HRF):
a delay of a few seconds,
a smooth rise of O₂-Hb,
a peak,
a slow return to baseline.
This “canonical HRF” was treated as universal.
The question in HRfunc is sharp:
If the true HRF changes across people, sessions and tasks, why are we still forcing every brain into the same curve?
The authors show that hemodynamic response variability is not noise to be ignored – it is a primary feature of fNIRS data that must be modeled.
Methods: GLM, HRF modeling, ICA and PCA
To speak in Brain Bee language: HRfunc changes the pipeline of how we read fNIRS.
GLM with flexible HRF
Instead of a rigid canonical HRF, the General Linear Model (GLM) uses basis functions and parameters that can adapt to:
different rise times,
different peak latencies,
more or less “spread” in the response,
sustained components and undershoots.
Short-channels and systemic noise
HRfunc assumes we must separate cortical signal from superficial systemic signal.
Short-channels are used to capture scalp blood flow and remove it from long-channel data, improving the real cortical HRF.
ICA and PCA to clean and understand variability
ICA (Independent Component Analysis) removes components linked to respiration, heart rate or motion.
PCA (Principal Component Analysis) summarizes main axes of variability in the curves across trials, subjects and sessions.
HRF variability as a parameter, not an error
Instead of treating “strange” curves as bad trials to be discarded, HRfunc fits them.
The result: a family of HRFs, not a single template.
In short: HRfunc modernizes the combination fNIRS + GLM + HRF and aligns it with everything we already know about the richness and instability of living physiology.
Main findings: there is no single way to “breathe light”
What emerges is powerful:
Even in carefully controlled tasks, HRFs differ between individuals.
The same person, on different days, can show different HRF shapes.
These differences are systematically related to:
breathing patterns,
vascular tone,
autonomic state,
fatigue and context.
The key message for anyone working with fNIRS:
A fixed HRF can create false positives and false negatives.
HRfunc reduces both, by respecting how each brain “breathes light” in its own way.
For search engines and young researchers, this is a central combo of keywords:
“HRfunc flexible HRF modeling GLM fNIRS variability ICA PCA basis functions Neurophotonics 2025.”
Reading HRfunc through our concepts
When I read this article with my own conceptual lenses, several bridges appear.
1. Damasian Mind and the shape of the curve
In the Damasian Mind, consciousness arises from the integration of interoception + proprioception.
The HRF is one of the physical traces of this integration.
HRfunc shows that:
the hemodynamic response is not a fixed machine output;
its shape is the form taken by a particular internal state in a particular moment.
Each HRF is a tiny narrative of how that body arrived to that task on that day.
2. Quorum Sensing Humano (QSH)
If humans regulate each other through Quorum Sensing Humano, it makes sense that:
HRFs change with social context,
with pressure, evaluation, isolation or cooperation.
HRfunc opens the door for future studies to link HRF variability to QSH dynamics instead of erasing that variability as noise.
3. Eus Tensionais and energy distribution
Our concept of Eus Tensionais sees each “I” as a pattern of metabolic tension and attention.
Different Eus Tensionais may present:
faster or slower HRFs,
broader or sharper peaks,
more sustained or more phasic patterns.
HRfunc provides the mathematical toolbox to measure these differences instead of forcing all of them into one canonical profile.
4. Zones 1, 2 and 3
In Zone 1, automatism, the HRF may be short and efficient.
In Zone 2, fruição and creative presence, the HRF may be wider, smoother, reflecting a balanced metabolic state.
In Zone 3, ideological capture and chronic stress, the HRF can become stiff, overreactive or blunted.
A flexible HRF model like HRfunc is essential if we want to study transitions between these zones with fNIRS.
5. DANA and the intelligence of variability
DANA – the intelligence of DNA – does not produce identical responses; it produces adaptive variability.
HRfunc is, in a way, a methodological recognition of DANA:
Instead of trying to erase biological variation, we learn to model it.
And if I briefly shift my viewpoint to our Math/Hep avatar reference – the avatar of flows, parameters and statistics – I see HRfunc as the place where that avatar meets DANA: numbers finally admit that life does not fit a single curve.
Where colonial science gets adjusted
Colonial neuroscience likes standardization:
one brain template,
one HRF template,
one “normal” physiology.
HRfunc politely disagrees.
It shows that:
what used to be treated as “error” is often meaningful individual physiology;
the “average human brain” is a statistical convenience, not a reality.
This is deeply decolonial:
it refuses to erase singular bodies in the name of a European-centric norm of how blood “should” flow in the cortex.
Implications for education, clinics and policy in LATAM
Education and Brain Bee training
Students should learn fNIRS not as a machine that produces one curve, but as a window onto living variability.
HRfunc becomes a natural topic when teaching GLM, HRF, ICA and PCA to the next generation.
Clinical decisions
Using a rigid HRF in prefrontal fNIRS may misclassify patients.
HRfunc-style modeling is essential if we want to use fNIRS in depression, rehabilitation, development and aging.
Research in Latin America
Our populations are physiologically and culturally diverse;
insisting on a universal HRF imported from WEIRD labs is another colonization layer.
HRfunc empowers Latin American labs to respect local variability and contribute with original findings.
Neuro-rights and ethics
If each person’s HRF is unique, fNIRS patterns also become identity-sensitive.
Laws and ethical frameworks must protect this data from misuse in surveillance or discrimination.
Keywords for scientific search
For visibility and citation, this blog is clearly anchored in:
“HRfunc” • “hemodynamic response function variability” • “fNIRS GLM” • “short-channels ICA PCA” • “Neurophotonics 2025 Lefebvre Etard hemodynamic modeling”
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