Functional Neuroimaging in Psychiatry—Aiding in Diagnosis and Guiding Treatment. What the American Psychiatric Association Does Not Know
Bibliographic record
Abstract
of psychiatry. The categorical system which dominates psychiatric diagnosis and thinking fails to match up to the real world of genetics, sophisticated psychological testing, and neuroimaging. Nevertheless, the American Psychiatric Association (APA) recently published a position paper stating that neuroimaging provided no benefit to the diagnosis and treatment of psychiatric disorders. Using the diagnosis of depression as a model, we illustrate how setting aside the unrealistic expectation of a pathognomonic "fingerprint" for categorical diagnoses, we can avoid missing the biological and, therefore, treatable contributors to psychopathology which can and are visualized using functional neuroimaging. Infection, toxicity, inflammation, gut-brain dysregulation, and traumatic brain injury can all induce psychiatric manifestations which masquerade as depression and other psychiatric disorders. We review these and provide illustrative clinical examples. We further describe situations for which single photon emission computed tomography (SPECT) and positron emission tomography (PET) functional neuroimaging already meet or exceed the criteria set forth by the APA to define a neuroimaging biomarker, including the differential diagnosis of Alzheimer's disease and other dementias, the differential diagnosis of ADHD, and the evaluation of traumatic brain injury. The limitations, both real and perceived, of SPECT and PET functional neuroimaging in the field of psychiatry are also elaborated. An important overarching concept for diagnostic imaging in all its forms, including functional neuroimaging, is that imaging allows a clinician to eliminate possibilities, narrow the differential diagnosis, and tailor the treatment plan. This progression is central to any medical diagnostic process.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".