Neurotechnology in ADHD Diagnosis: A Research on Innovations and Applications
Bibliographic record
Abstract
Abstract. Attention Deficit Hyperactivity Disorder (ADHD) is characterized by persistent symptoms of hyperactivity, impulsivity, and inattention, presenting significant challenges in daily functioning. Diagnosing ADHD, particularly in adulthood, can be intricate due to symptom overlap with other disorders and the subjective nature of behavioral assessments. However, advancements in neuroimaging techniques have illuminated the neurological underpinnings of ADHD, offering a more objective approach to understanding and diagnosing this neurodevelopmental condition. Neuroimaging technologies such as Electroencephalography (EEG), Near-Infrared Spectroscopy (NIRS), and Functional Magnetic Resonance Imaging (fMRI) have been pivotal in mapping the brain's functional and structural anomalies associated with ADHD.This review synthesizes the application of neuroimaging in the diagnosis of ADHD, encompassing EEG, fMRI, and NIRS. It discusses the potential and limitations of these technologies in assessing brain function and structure alterations in individuals with ADHD and explores their prospects for clinical application. As we conclude and look ahead, the continuous progress in technology and research is set to make neuroimaging an increasingly vital component in the diagnosis and treatment of ADHD. It promises to deliver more personalized and precise therapeutic approaches for individuals affected by this disorder.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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 source (direct Gemma or distilled Codex), 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".