Neuroscientists have been mapping the networks of the human brain for decades, and they keep coming up with different models. Seven networks. Seventeen networks. Twenty-four networks. Thirty-three networks. A hundred networks.

And the thing is, none are wrong, and they're all right.

It's like getting five cartographers to map the same city and getting back five completely different maps. Except in this case, the city is your brain, and the maps determine how we understand everything from why you can't focus in meetings to why your anxiety won't shut up at 3 AM.

So I did what any reasonable person would do and figured out how they all overlap.

The different "networks" aren't contradictions, they're just different resolutions of the same underlying structure. It's like zooming in on Google Maps. At the country level, you see major highways. Zoom in, and suddenly you see streets. Zoom in further, and you see individual buildings. Same city, different levels of detail.

To help me understand how this field is developing, I decided to create a unified framework that showed how all these different "maps" relate to each other.

Let me introduce you to what I'm calling the NeuroCogniX Connectome Framework; a hierarchical, non-redundant organization of every functional brain network discovered across the major connectomics studies.

And this isn't just academic navel-gazing. Understanding how these networks work has profound implications for how you run your business, manage your health, and make decisions that actually stick.

A Brief History of Mapping the Brain

The idea that we could map the brain's functional networks started gaining serious traction in 2005, when Olaf Sporns, Giulio Tononi, and Rolf Kötter published a paper introducing the term "connectome", which is a comprehensive map of neural connections in the brain. Think of it as the brain's wiring diagram.

But here's where it gets interesting. Unlike previous attempts to understand the brain by studying individual regions in isolation, the connectome approach recognized something crucial: the brain works as a distributed network of networks. Your ability to read this sentence right now isn't happening in one spot in your brain, it's the coordinated activity of multiple brain regions working together in real-time.

In 2010, the USA National Institutes of Health launched the Human Connectome Project with a simple but ambitious goal: map the complete structural and functional connectivity of the healthy human brain. They initially threw $40 million at this, eventually scaling to over $100 million. The project recruited over 1,200 healthy adults, scanned their brains for hours using cutting-edge neuroimaging, and generated datasets so massive that analyzing them required supercomputers.

The result? A revolution in how we understand the brain. But also, as I mentioned, a bit of chaos.

Different research groups, using slightly different methods and asking slightly different questions, started publishing their own network "atlases." In 2011, Thomas Yeo and colleagues published what became the most widely used framework: seven major functional networks. Then they published a finer-grained version with seventeen networks. Then another group published twenty-four networks. Then thirty-three. Then precision mapping studies showed that individuals can have fifty or more personalized networks.

Since I'm working with entrepreneurs and professionals who need to understand why their decision-making goes sideways under stress, and with patients who need to know which brain networks are disrupted in their mental health, chronic pain or neuromotor disorders I went through every major atlas, Yeo's 7 and 17 networks, the Dev-Atlas 24 networks for adolescents, the GINNA 33 networks, and dozens of specialized studies, and mapped how they all relate to each other.

What emerged was a four-level hierarchical framework that eliminates redundancy while preserving every level of detail.

Your Brain's Table of Contents

Think of your brain as having an operating system with eight major functional systems. Each system has primary networks, which subdivide into subnetworks, which can further subdivide into specialized modules. It's hierarchical, like a corporate org chart, except instead of departments, you have neural networks that control everything from whether you notice that email notification to whether you say “Yes” to a business deal.

The Four Levels of Organization:

Total: 60-70 distinct, non-redundant functional networks (instead of the "100" when you count duplicates across atlases).

The Complete NeuroCogniX Connecctome Atlas

This is the reference table that shows every network, what it does, where it lives in your brain, and how it maps to the different atlases you'll encounter in the literature.

(Pro tip: Bookmark this. You'll want to come back to it. Or, download the PDF using the button below)

How to Use This Table (Because It's Not Just Pretty)

Let's be honest, that's a lot of information. But here's why it matters and how to actually use it.

If you're reading a research paper and they mention "the salience network," you can look it up (NC-A3.1) and see exactly what brain regions they're talking about, what it does, and how it relates to other atlases. No more guessing whether "salience network" means the same thing across different studies.

If you're trying to understand a clinical condition, you can identify which systems are disrupted. Depression? Look at NC-D1 (Default Mode = hyperactive rumination), NC-E1 (Executive Control = weak regulation), and NC-L2 (Reward System = reduced motivation). Now you know which networks to target with an intervention.

If you're optimizing performance, you can identify your weak links. Struggle with focus? That's NC-A1 (Dorsal Attention) and NC-E1 (Executive Control).

Struggle with emotional regulation? That's NC-L1.4 (Emotion Regulation Network).

Match the intervention to the network.

If you're designing a study or intervention, you can choose the appropriate resolution. Broad intervention? Work at L1-L2. Targeted intervention like tDCS? Work at L3-L4 to identify specific anatomical targets.

Why This Matters for Business (And Why I'm Telling You This)

You didn't come here for a neuroscience lecture. You came here because you want to know how this applies to the real world. Fair enough.

Here's where it gets practical.

Every decision you make, every strategy you execute, every conversation you have with a client or employee involves the coordinated activity of multiple brain networks. Understanding which networks are active, and in what sequence, gives you a massive advantage.

Take sales, for example. I've spent years working with entrepreneurs and founders who are brilliant at creating products but struggle to sell them. The standard advice is usually some variation of "show them the value" or "overcome their objections." But here's what neuroscience actually shows:

The sequence of network activation matters more than the content.

If you activate someone's pain/problem networks first (NC-A3.1 Salience, NC-L1.3 Emotion Processing), you get 2–3 times stronger engagement than if you immediately try to activate their reward networks. This is because the brain is wired to prioritize the avoidance of potential losses over the pursuit of equivalent gains, a phenomenon known as loss aversion.

Importantly, this doesn’t require an actual loss. The mere anticipation of cost or threat, such as the possibility of wasting time, money, or missing out, triggers heightened activity in the amygdala (NC-L1.3) and anterior insula (NC-A3.1). These regions respond more strongly to potential losses than to equivalent gains, making problem-first framing a powerful way to engage attention and motivate action.

Then, if you get them to articulate their own desired outcome (activating NC-D1.1 Default Mode for future thinking and NC-L2.1 Reward System for anticipated gains), you create self-generated goals, which produce stronger commitment than externally imposed goals. Their ventral striatum lights up more when they generate the goal themselves.

Then, if you use mental contrasting, by having them imagine both the desired outcome AND the obstacles in the way, you activate their motor planning networks (NC-M2.1 Supplementary Motor) and autonomic system (NC-H1), literally priming their body for action before they consciously decide to buy.

This isn't manipulation. This is alignment with how the brain naturally makes decisions.

I've used this framework to help founders go from struggling to close deals to having prospects ask to buy. Not because they learned some clever sales script, but because they learned to work with the brain's decision-making architecture instead of against it.

Why This Matters for Health (And Why Your Doctor Probably Doesn't Know This Yet)

Here's something that might blow your mind: most neurological and psychiatric disorders aren't "broken brain regions"; they're disrupted brain networks.

Depression isn't a serotonin deficiency. It's hyperactivity in NC-D1.1 (Core Default Mode = the rumination network), reduced connectivity between NC-E1.1 (Central Executive) and NC-L1.3 (Emotion Processing), and often disrupted NC-L2 (Reward System) function. That's why the same medication doesn't work for everyone. Different people have different network disruption patterns.

Chronic pain? Not just a sensory problem. It involves NC-A3.1 (Salience Network = which amplifies pain signals), NC-S3.3 (Interoceptive Network = which monitors body states), NC-D1.1 (Default Mode = which creates the narrative of suffering), and NC-L1.3 (Emotion Processing = which adds emotional weight). Treating chronic pain effectively means addressing the network, not just the sensation.

Parkinson's Disease is a classic "connectopathy"; a disorder of brain network connectivity. The motor symptoms everyone recognizes (NC-M3 Basal Ganglia Motor Loop disruption) are just the tip of the iceberg. The real story is disrupted connectivity in the cortico-basal ganglia-thalamocortical loops, which affects not just movement but also NC-E2 (cognitive function), NC-L3 (emotion and motivation), and NC-H1 (autonomic function).

This is why interventions like transcranial direct current stimulation (tDCS) can produce immediate clinical changes in Parkinson's patients. You're not fixing damaged neurons, you're re-tuning dysfunctional networks. The electrical stimulation at the scalp propagates through the brain's connectivity, modulating activity in subcortical structures you can't directly reach.

I've seen this firsthand in clinical practice. When we understand these conditions as a network disorder, rather than a localized brain problem, we approach treatment differently. We’re more open to multimodal interventions. We understand why exercise, cognitive training, and neuromodulation might all be part of the solution, because they're all ways of influencing network function.

The Resolution Problem

There's no single "correct" number of brain networks.

The seven-network model (Yeo 2011) is perfect for understanding broad functional systems. It's like looking at a map of the United States and seeing major regions; Northeast, Southeast, Midwest, etc. Useful for high-level understanding.

The seventeen-network model is better when you need more detail. It’s like seeing individual states instead of just regions.

The twenty-four-network model (Dev-Atlas) captures adolescent brain organization with even finer resolution.

The thirty-three-network model (GINNA) provides empirically-derived cognitive characterizations for each network based on meta-analysis of thousands of neuroimaging studies.

And precision mapping studies show that individuals have unique network topographies. In fact, your brain's network organization isn't exactly the same as mine.

So which one is "right"? All of them. And none of them.

The NeuroCogniX Connectome Framework doesn't try to pick a winner. Instead, it shows you how they all relate to each other. It's a translation layer that lets you move between different resolutions depending on what you need.

If you're a researcher designing a neuroimaging study, you might work at the L3 level (33 networks). If you're a clinician trying to explain to a patient why their anxiety won't shut up, you might work at the L2 level (NC-D1 overactive Default Mode, NC-E1 underactive Executive Control). If you're an entrepreneur trying to understand decision-making, you might focus on specific subnetworks within NC-E1 (Executive Control) and NC-L2 (Reward Systems).

The framework gives you the flexibility to zoom in and out as needed, while always maintaining the connection between levels.

What This Means for You (The Practical Stuff)

Let me bring this home with some concrete applications.

For Business Leaders and Entrepreneurs:

Understanding brain networks gives you a massive advantage in three areas:

Decision-Making Under Pressure When you're stressed, NC-E1 (Executive Control) gets hijacked by NC-A3 (Salience) and NC-L1.3 (Emotion Processing). Knowing this lets you build systems that compensate; (1) structured decision frameworks, (2) pre-commitment strategies, and (3) environmental design that reduces cognitive load.

Team Performance Different people have different network profiles. Some people have strong NC-E1 (Executive Control) but weak NC-D1 suppression (they're great at focused work but struggle with mind-wandering). Others have strong NC-A3 (Salience) but weak NC-E1 (they notice everything but struggle to filter). Understanding this helps you build complementary teams and assign roles that match neural strengths.

Persuasion and Influence Whether you're selling, negotiating, or leading, understanding the sequence of network activation gives you a roadmap. You're not guessing, you're working with the brain's natural decision-making architecture.

For Health and Wellness:

Understanding your own network function helps you:

Identify Your Weak Points Do you struggle with rumination? That's NC-D1 hyperactivity. Do you struggle with impulse control? That's NC-E1-NC-L1.3 connectivity. Knowing the network helps you target the intervention.

Choose Effective Interventions Not all interventions work for all network disruptions. Meditation strengthens NC-E1-NC-D1 connectivity. Exercise improves NC-L2 (Reward System) function. Cognitive behavioral therapy rewires NC-L1.3-NC-E1 connections. Match the intervention to the network.

Track Progress Objectively Instead of vague goals like "feel less anxious," you can target specific network functions: "Reduce NC-D1 hyperactivity" or "Strengthen NC-E1-NC-L1.3 connectivity." This makes progress measurable.

For Parents and Educators:

The adolescent brain (Dev-Atlas networks) shows us that teenage behavior isn't just "being difficult"; it's incomplete network maturation. NC-E1 (Executive Control) is still developing while NC-L2 (Reward System) is in overdrive. Understanding this changes how you approach adolescent decision-making, risk-taking, and emotional regulation.

A Living Unfinished Framework

The NeuroCogniX framework is designed to evolve.

As new research emerges, as precision mapping studies reveal more individual variability, as we discover new specialized networks, the framework can incorporate them without breaking. It's hierarchical and modular, and new discoveries will slot into the existing structure.

If you're a researcher and you spot something I've missed, let me know. If you're a clinician and you've found a practical application I haven't considered, share it. If you're an entrepreneur and you've used this framework to solve a real-world problem, I want to hear about it.

The Bottom Line

Your brain isn't a collection of isolated regions doing their own thing. It's an integrated network of networks, with different systems coordinating in real-time to produce everything you think, feel, and do.

Understanding this network architecture isn't just academic, it's practical. It changes how you make decisions, how you lead teams, how you sell, how you treat illness, and how you optimize performance.
The NeuroCogniX framework gives you a unified map of this territory. Not the only map, but a map that shows you how all the other maps relate to each other.

And trust me, as someone who's spent years translating brain science into practical tools, having a good map makes all the difference.

Because here's the thing: your brain is already using these networks. Every day. Every decision. Every conversation. Every moment.

The question is whether you're going to understand how they work, or just hope for the best.

I know which option I prefer.

References and Further Reading

The Human Connectome Project:

• Van Essen, D.C., et al. (2013). The WU-Minn Human Connectome Project: An overview. NeuroImage, 80, 62-79. 
• Glasser, M.F., et al. (2016). A multi-modal parcellation of human cerebral cortex. Nature, 536(7615), 171-178.
• Elam, J.S., et al. (2021). The Human Connectome Project: A retrospective. NeuroImage, 244, 118543.

Major Network Atlases:

• Yeo, B.T., et al. (2011). The organization of the human cerebral cortex estimated by intrinsic functional connectivity. Journal of Neurophysiology, 106(3), 1125-1165.
• Schaefer, A., et al. (2018). Local-global parcellation of the human cerebral cortex from intrinsic functional connectivity MRI. Cerebral Cortex, 28(9), 3095-3114.
• Doucet, G.E., et al. (2025). Dev-Atlas: A reference atlas of functional brain networks for typically developing adolescents. Developmental Cognitive Neuroscience, 71, 101483.
• Gillig, A., et al. (2025). GINNA, a 33 resting-state networks atlas with meta-analytic decoding-based cognitive characterization. Communications Biology, 8, 72.

Foundational Connectomics:

• Sporns, O., Tononi, G., & Kötter, R. (2005). The human connectome: A structural description of the human brain. PLoS Computational Biology, 1(4), e42.
• Sporns, O. (2011). The human connectome: A complex network. Annals of the New York Academy of Sciences, 1224(1), 109-125.
• Sporns, O. (2013). Network attributes for segregation and integration in the human brain. Current Opinion in Neurobiology, 23(2), 162-171.

Clinical Applications (Connectopathies):

• Fornito, A., Zalesky, A., & Breakspear, M. (2015). The connectomics of brain disorders. Nature Reviews Neuroscience, 16(3), 159-172.
• Crossley, N.A., et al. (2014). The hubs of the human connectome are generally implicated in the anatomy of brain disorders. Brain, 137(8), 2382-2395.

Decision Neuroscience and Neuroeconomics:

• Knutson, B., et al. (2007). Neural predictors of purchases. Neuron, 53(1), 147-156.
• Rangel, A., Camerer, C., & Montague, P.R. (2008). A framework for studying the neurobiology of value-based decision making. Nature Reviews Neuroscience, 9(7), 545-556.
• Hare, T.A., Camerer, C.F., & Rangel, A. (2009). Self-control in decision-making involves modulation of the vmPFC valuation system. Science, 324(5927), 646-648.

Network Dynamics and Temporal Processing:

• Larsen, T., & O'Doherty, J.P. (2014). Uncovering the spatio-temporal dynamics of value-based decision-making in the human brain. Philosophical Transactions of the Royal Society B, 369(1655), 20130473.
• Gluth, S., Rieskamp, J., & Büchel, C. (2012). Deciding when to decide: time-variant sequential sampling models explain the emergence of value-based decisions in the human brain. Journal of Neuroscience, 32(31), 10686-10698.

Mental Contrasting and Goal Achievement:

• Oettingen, G. (2014). Rethinking Positive Thinking: Inside the New Science of Motivation. Current.
• Oettingen, G., & Gollwitzer, P.M. (2010). Strategies of setting and implementing goals: Mental contrasting and implementation intentions. Social Psychological and Personality Science, 1(2), 111-119.

Loss Aversion and Emotion in Decision-Making:

• Canessa, N., et al. (2013). The functional and structural neural basis of individual differences in loss aversion. Journal of Neuroscience, 33(36), 14307-14317.
• Damasio, A.R. (1994). Descartes' Error: Emotion, Reason, and the Human Brain. Putnam.
• Bechara, A., et al. (1997). Deciding advantageously before knowing the advantageous strategy. Science, 275(5304), 1293-1295.

Precision Functional Mapping:

• Hermosillo, R.J.M., et al. (2024). A precision functional atlas of personalized network topography and probabilities. Nature Neuroscience, 27, 1000-1013.
• Gordon, E.M., et al. (2017). Precision functional mapping of individual human brains. Neuron, 95(4), 791-807.

Network Neuroscience Methods:

• Bassett, D.S., & Sporns, O. (2017). Network neuroscience. Nature Neuroscience, 20(3), 353-364.
• Bullmore, E., & Sporns, O. (2009). Complex brain networks: graph theoretical analysis of structural and functional systems. Nature Reviews Neuroscience, 10(3), 186-198.