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Steven Rondeau BCN (EEG), qEEG-DL
The DSM offers outdated labels based on symptoms, not biology. qEEG and brain mapping provide objective insights that empower more accurate, personalized mental health care.
Mental health providers today are expected to treat with a map drawn decades ago. The DSM remains our default framework, not because it works well but because it’s what we’ve always done. However, diagnosis in psychiatry often creates a false sense of clarity. What looks like certainty is often guesswork cloaked in labels.
The DSM, or Diagnostic and Statistical Manual of Mental Disorders, is psychiatry’s primary diagnostic guide. Now in its fifth edition (DSM-5), the manual was originally developed in the 1950s based on surveys of clinicians and how they described and treated patients at the time. It wasn’t based on biology—it was founded on observed patterns of behavior and clinical consensus. While it brought structure and consistency to the field, it was never intended to reflect underlying brain function or physiology.
At the heart of modern psychiatric diagnosis is a paradox: we use subjective reports of internal experiences, rated on subjective scales, and interpreted through subjective clinician judgment to make decisions that often involve powerful medications, long-term treatment plans, and lasting labels. A patient may rate their anxiety as a 7 and another as a 9, but there is no benchmark for what “7” means biologically. Symptom checklists and rating scales were designed to approximate shared language but are not objective diagnostic tools. Asking a patient to rate their sadness on a scale of 1 to 10 is like asking someone to measure the warmth of a hug with a thermometer. It produces a number, but it doesn’t measure what we think it does. These tools reflect internal experience filtered through mood, memory, culture, and communication style, but not direct observation of function. They reflect patterns of distress, not patterns of dysfunction. Studies have repeatedly shown that rating scales lack specificity and reliability, particularly across diverse populations and cultures.1,2
Providers are not wrong, however, for relying on these tools because they’ve had very little else. The result is a system where arbitrary thresholds define entire identities, and subjective impressions guide pharmacologic choices. That isn’t precision medicine. It’s trial-and-error.
Some therapists worry that bringing in objective data might undercut their clinical instincts or their nuanced work in the room. In reality, the opposite is true. Tools like qEEG don’t replace expertise; rather, they support it. They give language to what many providers already sense but haven’t had a way to measure. For patients, I have witnessed, it can be deeply validating, especially in the case of substance use disorder (SUD) and addictions. Instead of feeling like their symptoms are just weakness or dysfunction, they see that there’s a physiological driver behind their struggles. Even when past treatments haven’t worked, having objective insight can help explain why and open new doors for patients and providers.
The DSM was created to help clinicians speak the same language. And it worked, to a degree. It brought structure to a field that once relied solely on narrative. It helped standardize research criteria, reduce chaos in clinical settings, and make mental health more visible in primary care. However, the system was never designed to measure underlying brain function. It was built around surface-level symptoms and not root causes. As a result, it often groups people who feel the same but function very differently. Research has consistently shown that DSM diagnoses do not reliably predict treatment outcomes. In major depression, for example, less than 40% of patients respond to first-line antidepressants even when their symptoms fit the DSM criteria perfectly.3
• Built for communication, not biological insight
• Labels often mask diverse underlying causes
• Poor predictor of treatment outcomes
• Still essential for coding and access, but incomplete for care
The issue isn’t a lack of care, but rather the false confidence the DSM can create. It looks precise, yet it doesn’t hold up when you sit with patients. Many clinicians already know this. They’ve seen treatments that were supposed to work, fail. They’ve changed diagnoses, adjusted meds, and done everything right—only to watch the patient stay stuck. Not because they missed something or poor patient compliance, but because the system is limited. Diagnosis can describe the problem, but in mental health, it rarely explains it. What we need are more tools that bring understanding. Tools like qEEG aren’t the answer to everything, but they help us see what’s happening physiologically. They give us a way to understand what we’ve been sensing all along, making good care possible.
Symptom overlap isn’t a side issue—it’s one of the core problems in psychiatric diagnosis. Conditions like depression and anxiety routinely share symptoms such as sleep disturbance, fatigue, and cognitive slowing. However, that similarity tells us almost nothing about what’s happening in the brain.
• Patient A: Diagnosed with depression, shows increased relative frontal theta and delta
• Patient B: Same diagnosis, but exhibits low relative beta activity and elevated frontal alpha
• Patient C: Same diagnosis, but peak alpha in the frontal sites and low PDR (posterior dominant rhythm)
Source: EEG Data Hub, de-identified clinical dataset (2025).
Recent research highlights just how disconnected symptoms and biology can be. Patients with a DSM diagnosis may not share the same symptoms at all.4 Another analysis found that individuals under the same disorder show wide-ranging symptom constellations, making the label nearly meaningless when guiding treatment.5 Studies have also shown that while clinically indistinguishable on intake, patients with depression or anxiety can present with very different EEG biomarkers. These differences point to vastly different neurophysiological underpinnings.6 Skeptics may look at that variability and question the validity of the test. The issue lies not with the tool, but with the framework we’re using to interpret the results. When objective data shows just how different patients are, even when they meet the same diagnostic criteria, it doesn’t undermine the test. This exposes the core problem in psychiatry that we’ve built the gold standard around a model that doesn’t reflect the underlying biology.
The DSM remains in use not because it reflects modern neuroscience but because it’s built into everything from academia to billing, training, licensure, and access to care. Diagnosis equals permission, but it doesn’t always equal understanding.
Psychiatry also has good reason to be cautious. Many of us remember how confidently the “chemical imbalance” theory was once promoted. This concept of mental illness came down to low serotonin or dopamine. While it made sense at the time, it was easy to explain, and it helped reduce stigma. However, it also oversimplified the reality of what patients were going through. We don’t discuss how that theory faded, but it did. The research continued, even if the public messaging didn’t7 from my experience, that left a mark on the field. It’s why clinicians are right to be skeptical of anything that sounds like a shortcut.
• Reimbursement and diagnostic coding requirements
• Institutional licensing and insurance constraints
• Legacy clinical trials designed on DSM criteria
• Provider training and systemic risk aversion
• Absence of scalable alternatives in traditional models
qEEG isn’t another theory. It shows us what the brain is doing in real time, rather than what we assume it’s doing based on symptoms. It lets us look past the surface and see patterns that help explain why someone’s struggling or didn’t respond to treatment the way we expected. For many providers, it confirms what they already sensed but couldn’t prove. That insight strengthens our clinical judgments and validates patients’ or parents’ experiences.
In most areas of medicine, when a treatment doesn’t work, the next step is to gather more data. You run labs, order imaging, and review systems. You look for what might’ve been missed. But in psychiatry, we’re often left with one option, which is to adjust the dose or switch the medication and hope for a better result. Clinicians are being asked to make decisions without the visibility that exists in nearly every other specialty of medicine.
Clinicians aren’t failing. They’re working without the tools they need. DSM codes can’t tell us whether inattention comes from an underaroused frontal cortex or an overstimulated one. They don’t show whether someone’s depression is driven by slowed cortical activity or excessive connectivity in the default mode network. We’re assigning the same labels to very different brain states, and then wondering why the treatments don’t always work. Instead of using the tools to better match treatments to the person, we keep chasing new interventions. Psychedelics, ketamine, and neuromodulation—these all hold promise. The issue isn’t a lack of treatment options – it’s the imprecision in how we apply them. Readily accessible biomarkers like qEEG can help us understand how someone’s brain is functioning, which makes it possible to stop throwing darts in the dark. We don’t need more medications—we need better maps.
qEEG lets us see the patterns beneath the symptoms. Two patients can both report anxiety, but their brains may be doing very different things. One might show high beta activity, racing thoughts, and excessive cortical activation. These are patterns that we usually think of as classic anxiety. The other might show excessive frontal slowing or low voltage alpha, which reflects an under-aroused or shut down nervous system that still feels anxious from the inside, a profile we refer to as overwhelm. The symptoms look the same on intake, but the treatment approach is vastly different. One may need calming and regulation. The other needs support for engagement and stimulation (like you might do with ADHD). Without objective data, we’re left guessing who needs what.
• Helps differentiate diagnostic subtypes
• Guides medication, therapy, neuromodulation, and botanical decisions
• Tracks changes over time
• Connects brain data to individual symptoms and systems
One of the unique aspects of EEG is its blunt neutrality. While it doesn’t diagnose (yet) or judge, it simply reflects how the brain functions in real-time. Unlike the DSM, which focuses solely on dysfunction, EEG data can also reveal areas of strength, such as cognitive flexibility, high creative potential, or enhanced attentional tuning. When used well, it becomes not just a diagnostic guide but also a map that can help individuals understand and refine their gifts to curate supportive environments, such as experiential learning schools or certain vocational tracks.
Mental illness rarely lives in isolation. It lives in nervous, immune, endocrine, and relational systems. A new model of care begins with better questions rather than an ICD-10 code. qEEG, acting like a lens paired with clinical reasoning and patient narrative, allows the provider to develop a roadmap rooted in pattern recognition, not protocol. It helps naturopathic and integrative clinicians tailor care plans that include herbs, nutrients, movement, neurofeedback, or lifestyle medicine without guesswork and in response to measurable brain function. While two people can share the same brain patterns, one may suffer when the other excels. What is the difference? It’s in the environment, the stress load, the meaning made from experience, and the internal systems shaping adaptation.
This is what I call the “husky phenomenon.” You can take two dogs with the same genetics, drive, and physiology. Put one in Alaska with a sled team, and he thrives. Put the other in a Florida condo with no job and a retractable leash, and he chews through drywall. The problem isn’t the dog; it’s the mismatch between pattern and environment. Human brains work the same way. What looks like pathology in one setting might be a strength in another. qEEG doesn’t tell us who’s broken. It tells us how someone is wired so we can ask the right questions about where that wiring fits, what it needs, and how it’s being shaped.
Mental health providers are already doing some of the most challenging work in medicine. They deserve better tools. While precision doesn’t erase complexity, we reduce trial-and-error, burnout, and misfires using tools that reveal physiology and systems patterns. Providers gain insight that helps them explain, intervene, and support with more clarity and fewer surprises, honoring the patient’s strengths and heterogeneity.
There’s growing excitement around novel interventions we have seen, such as psychedelics, ketamine, and neuromodulation. Each offers promise. However, none can bypass the need for precision, without frameworks that predict who will respond and why, even these exciting therapies risk repeating the past. Precision must come before novelty. Otherwise, we’re just throwing better darts at the same blindfolded target.
It may come as a surprise to patients (and some providers) that we still diagnose mental illness the way Abraham Lincoln was diagnosed in the 1830s by the shape of suffering, not its source. The DSM helped us name pain, but it never helped us understand it. We’re at the edge of the old map. The flat earth model has taken us far enough. We now have tools that let us see more clearly, act more intentionally, and support more precisely. This is how we move forward and leave the flat earth behind.
Dr. Steven Rondeau, BCN, QEEG-DL, is the founder of Axon and sole creator of EEG Data Hub, a clinical decision-support platform for brain-based mental health care. He holds patents in neurodiagnostics, has authored multiple peer-reviewed articles, and his forthcoming book, The Great Gamble, explores the urgent need to rethink psychiatric diagnosis. Dr. Rondeau provides qEEG interpretation and clinical insight to integrative and functional medicine providers worldwide, helping translate complex brain data into practical treatment direction. His work continues to guide the shift toward more precise, personalized, and objective models of mental health care.
Steven Rondeau BCN (EEG), qEEG-DL
The DSM offers outdated labels based on symptoms, not biology. qEEG and brain mapping provide objective insights that empower more accurate, personalized mental health care.
Mental health providers today are expected to treat with a map drawn decades ago. The DSM remains our default framework, not because it works well but because it’s what we’ve always done. However, diagnosis in psychiatry often creates a false sense of clarity. What looks like certainty is often guesswork cloaked in labels.
The DSM, or Diagnostic and Statistical Manual of Mental Disorders, is psychiatry’s primary diagnostic guide. Now in its fifth edition (DSM-5), the manual was originally developed in the 1950s based on surveys of clinicians and how they described and treated patients at the time. It wasn’t based on biology—it was founded on observed patterns of behavior and clinical consensus. While it brought structure and consistency to the field, it was never intended to reflect underlying brain function or physiology.
At the heart of modern psychiatric diagnosis is a paradox: we use subjective reports of internal experiences, rated on subjective scales, and interpreted through subjective clinician judgment to make decisions that often involve powerful medications, long-term treatment plans, and lasting labels. A patient may rate their anxiety as a 7 and another as a 9, but there is no benchmark for what “7” means biologically. Symptom checklists and rating scales were designed to approximate shared language but are not objective diagnostic tools. Asking a patient to rate their sadness on a scale of 1 to 10 is like asking someone to measure the warmth of a hug with a thermometer. It produces a number, but it doesn’t measure what we think it does. These tools reflect internal experience filtered through mood, memory, culture, and communication style, but not direct observation of function. They reflect patterns of distress, not patterns of dysfunction. Studies have repeatedly shown that rating scales lack specificity and reliability, particularly across diverse populations and cultures.1,2
Providers are not wrong, however, for relying on these tools because they’ve had very little else. The result is a system where arbitrary thresholds define entire identities, and subjective impressions guide pharmacologic choices. That isn’t precision medicine. It’s trial-and-error.
Some therapists worry that bringing in objective data might undercut their clinical instincts or their nuanced work in the room. In reality, the opposite is true. Tools like qEEG don’t replace expertise; rather, they support it. They give language to what many providers already sense but haven’t had a way to measure. For patients, I have witnessed, it can be deeply validating, especially in the case of substance use disorder (SUD) and addictions. Instead of feeling like their symptoms are just weakness or dysfunction, they see that there’s a physiological driver behind their struggles. Even when past treatments haven’t worked, having objective insight can help explain why and open new doors for patients and providers.
The DSM was created to help clinicians speak the same language. And it worked, to a degree. It brought structure to a field that once relied solely on narrative. It helped standardize research criteria, reduce chaos in clinical settings, and make mental health more visible in primary care. However, the system was never designed to measure underlying brain function. It was built around surface-level symptoms and not root causes. As a result, it often groups people who feel the same but function very differently. Research has consistently shown that DSM diagnoses do not reliably predict treatment outcomes. In major depression, for example, less than 40% of patients respond to first-line antidepressants even when their symptoms fit the DSM criteria perfectly.3
• Built for communication, not biological insight
• Labels often mask diverse underlying causes
• Poor predictor of treatment outcomes
• Still essential for coding and access, but incomplete for care
The issue isn’t a lack of care, but rather the false confidence the DSM can create. It looks precise, yet it doesn’t hold up when you sit with patients. Many clinicians already know this. They’ve seen treatments that were supposed to work, fail. They’ve changed diagnoses, adjusted meds, and done everything right—only to watch the patient stay stuck. Not because they missed something or poor patient compliance, but because the system is limited. Diagnosis can describe the problem, but in mental health, it rarely explains it. What we need are more tools that bring understanding. Tools like qEEG aren’t the answer to everything, but they help us see what’s happening physiologically. They give us a way to understand what we’ve been sensing all along, making good care possible.
Symptom overlap isn’t a side issue—it’s one of the core problems in psychiatric diagnosis. Conditions like depression and anxiety routinely share symptoms such as sleep disturbance, fatigue, and cognitive slowing. However, that similarity tells us almost nothing about what’s happening in the brain.
• Patient A: Diagnosed with depression, shows increased relative frontal theta and delta
• Patient B: Same diagnosis, but exhibits low relative beta activity and elevated frontal alpha
• Patient C: Same diagnosis, but peak alpha in the frontal sites and low PDR (posterior dominant rhythm)
Source: EEG Data Hub, de-identified clinical dataset (2025).
Recent research highlights just how disconnected symptoms and biology can be. Patients with a DSM diagnosis may not share the same symptoms at all.4 Another analysis found that individuals under the same disorder show wide-ranging symptom constellations, making the label nearly meaningless when guiding treatment.5 Studies have also shown that while clinically indistinguishable on intake, patients with depression or anxiety can present with very different EEG biomarkers. These differences point to vastly different neurophysiological underpinnings.6 Skeptics may look at that variability and question the validity of the test. The issue lies not with the tool, but with the framework we’re using to interpret the results. When objective data shows just how different patients are, even when they meet the same diagnostic criteria, it doesn’t undermine the test. This exposes the core problem in psychiatry that we’ve built the gold standard around a model that doesn’t reflect the underlying biology.
The DSM remains in use not because it reflects modern neuroscience but because it’s built into everything from academia to billing, training, licensure, and access to care. Diagnosis equals permission, but it doesn’t always equal understanding.
Psychiatry also has good reason to be cautious. Many of us remember how confidently the “chemical imbalance” theory was once promoted. This concept of mental illness came down to low serotonin or dopamine. While it made sense at the time, it was easy to explain, and it helped reduce stigma. However, it also oversimplified the reality of what patients were going through. We don’t discuss how that theory faded, but it did. The research continued, even if the public messaging didn’t7 from my experience, that left a mark on the field. It’s why clinicians are right to be skeptical of anything that sounds like a shortcut.
• Reimbursement and diagnostic coding requirements
• Institutional licensing and insurance constraints
• Legacy clinical trials designed on DSM criteria
• Provider training and systemic risk aversion
• Absence of scalable alternatives in traditional models
qEEG isn’t another theory. It shows us what the brain is doing in real time, rather than what we assume it’s doing based on symptoms. It lets us look past the surface and see patterns that help explain why someone’s struggling or didn’t respond to treatment the way we expected. For many providers, it confirms what they already sensed but couldn’t prove. That insight strengthens our clinical judgments and validates patients’ or parents’ experiences.
In most areas of medicine, when a treatment doesn’t work, the next step is to gather more data. You run labs, order imaging, and review systems. You look for what might’ve been missed. But in psychiatry, we’re often left with one option, which is to adjust the dose or switch the medication and hope for a better result. Clinicians are being asked to make decisions without the visibility that exists in nearly every other specialty of medicine.
Clinicians aren’t failing. They’re working without the tools they need. DSM codes can’t tell us whether inattention comes from an underaroused frontal cortex or an overstimulated one. They don’t show whether someone’s depression is driven by slowed cortical activity or excessive connectivity in the default mode network. We’re assigning the same labels to very different brain states, and then wondering why the treatments don’t always work. Instead of using the tools to better match treatments to the person, we keep chasing new interventions. Psychedelics, ketamine, and neuromodulation—these all hold promise. The issue isn’t a lack of treatment options – it’s the imprecision in how we apply them. Readily accessible biomarkers like qEEG can help us understand how someone’s brain is functioning, which makes it possible to stop throwing darts in the dark. We don’t need more medications—we need better maps.
qEEG lets us see the patterns beneath the symptoms. Two patients can both report anxiety, but their brains may be doing very different things. One might show high beta activity, racing thoughts, and excessive cortical activation. These are patterns that we usually think of as classic anxiety. The other might show excessive frontal slowing or low voltage alpha, which reflects an under-aroused or shut down nervous system that still feels anxious from the inside, a profile we refer to as overwhelm. The symptoms look the same on intake, but the treatment approach is vastly different. One may need calming and regulation. The other needs support for engagement and stimulation (like you might do with ADHD). Without objective data, we’re left guessing who needs what.
• Helps differentiate diagnostic subtypes
• Guides medication, therapy, neuromodulation, and botanical decisions
• Tracks changes over time
• Connects brain data to individual symptoms and systems
One of the unique aspects of EEG is its blunt neutrality. While it doesn’t diagnose (yet) or judge, it simply reflects how the brain functions in real-time. Unlike the DSM, which focuses solely on dysfunction, EEG data can also reveal areas of strength, such as cognitive flexibility, high creative potential, or enhanced attentional tuning. When used well, it becomes not just a diagnostic guide but also a map that can help individuals understand and refine their gifts to curate supportive environments, such as experiential learning schools or certain vocational tracks.
Mental illness rarely lives in isolation. It lives in nervous, immune, endocrine, and relational systems. A new model of care begins with better questions rather than an ICD-10 code. qEEG, acting like a lens paired with clinical reasoning and patient narrative, allows the provider to develop a roadmap rooted in pattern recognition, not protocol. It helps naturopathic and integrative clinicians tailor care plans that include herbs, nutrients, movement, neurofeedback, or lifestyle medicine without guesswork and in response to measurable brain function. While two people can share the same brain patterns, one may suffer when the other excels. What is the difference? It’s in the environment, the stress load, the meaning made from experience, and the internal systems shaping adaptation.
This is what I call the “husky phenomenon.” You can take two dogs with the same genetics, drive, and physiology. Put one in Alaska with a sled team, and he thrives. Put the other in a Florida condo with no job and a retractable leash, and he chews through drywall. The problem isn’t the dog; it’s the mismatch between pattern and environment. Human brains work the same way. What looks like pathology in one setting might be a strength in another. qEEG doesn’t tell us who’s broken. It tells us how someone is wired so we can ask the right questions about where that wiring fits, what it needs, and how it’s being shaped.
Mental health providers are already doing some of the most challenging work in medicine. They deserve better tools. While precision doesn’t erase complexity, we reduce trial-and-error, burnout, and misfires using tools that reveal physiology and systems patterns. Providers gain insight that helps them explain, intervene, and support with more clarity and fewer surprises, honoring the patient’s strengths and heterogeneity.
There’s growing excitement around novel interventions we have seen, such as psychedelics, ketamine, and neuromodulation. Each offers promise. However, none can bypass the need for precision, without frameworks that predict who will respond and why, even these exciting therapies risk repeating the past. Precision must come before novelty. Otherwise, we’re just throwing better darts at the same blindfolded target.
It may come as a surprise to patients (and some providers) that we still diagnose mental illness the way Abraham Lincoln was diagnosed in the 1830s by the shape of suffering, not its source. The DSM helped us name pain, but it never helped us understand it. We’re at the edge of the old map. The flat earth model has taken us far enough. We now have tools that let us see more clearly, act more intentionally, and support more precisely. This is how we move forward and leave the flat earth behind.
Dr. Steven Rondeau, BCN, QEEG-DL, is the founder of Axon and sole creator of EEG Data Hub, a clinical decision-support platform for brain-based mental health care. He holds patents in neurodiagnostics, has authored multiple peer-reviewed articles, and his forthcoming book, The Great Gamble, explores the urgent need to rethink psychiatric diagnosis. Dr. Rondeau provides qEEG interpretation and clinical insight to integrative and functional medicine providers worldwide, helping translate complex brain data into practical treatment direction. His work continues to guide the shift toward more precise, personalized, and objective models of mental health care.
It is a long established fact that a reader will be distracted by the readable content of a page when looking at its layout. The point of using Lorem Ipsum is that it has a more-or-less normal distribution of letters, as opposed to using ‘Content here, content here’, making it look like readable English. Many desktop publishing packages and web page editors now use Lorem Ipsum as their default model text, and a search for ‘lorem ipsum’ will uncover many web sites still in their infancy.
It is a long established fact that a reader will be distracted by the readable content of a page when looking at its layout. The point of using Lorem Ipsum is that it has a more-or-less normal distribution of letters, as opposed to using ‘Content here, content here’, making it look like readable English. Many desktop publishing packages and web page editors now use Lorem Ipsum as their default model text, and a search for ‘lorem ipsum’ will uncover many web sites still in their infancy.
The point of using Lorem Ipsum is that it has a more-or-less normal distribution of letters, as opposed to using ‘Content here, content here’, making
The point of using Lorem Ipsum is that it has a more-or-less normal distribution of letters, as opposed to using ‘Content here, content here’, making it look like readable English. Many desktop publishing packages and web page editors now use Lorem Ipsum as their default model text, and a search for ‘lorem ipsum’ will uncover many web sites still in their infancy.
It is a long established fact that a reader will be distracted by the readable content of a page when looking at its layout. The point of using Lorem Ipsum is that it has a more-or-less normal distribution
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