Neural correlates of N‐back task performance and proposal for corresponding neuromodulation targets in psychiatric and neurodevelopmental disorders
Bibliographic record
Abstract
AIM: Working memory (WM) deficit represents the most common cognitive impairment in psychiatric and neurodevelopmental disorders, making the identification of its neural substrates a crucial step towards the conceptualization of restorative interventions. We present a meta-analysis focusing on neural activations associated with the most commonly used task to measure WM, the N-back task, in patients with schizophrenia, depressive disorder, bipolar disorder, and attention-deficit/hyperactivity disorder. Showing qualitative similarities and differences in WM processing between patients and healthy controls, we propose possible targets for cognitive enhancement approaches. METHODS: Selected studies, following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines, were analyzed through the activation likelihood estimate statistical framework, with subsequent generation of disorder-specific N-back activation maps. RESULTS: Despite similar WM deficits shared across all disorders, results highlighted different brain activation patterns for each disorder compared with healthy controls. In general, results showed brain activity in frontal, parietal, subcortical, and cerebellar regions; however, reduced engagement of specific nodes of the fronto-parietal network emerged in patients compared with healthy controls. In particular, neither bipolar nor depressive disorders showed detectable activations in the dorsolateral prefrontal cortices, while their parietal activation patterns were lateralized to the left and right hemispheres, respectively. On the other hand, patients with attention-deficit/hyperactivity disorder showed a lack of activation in the left parietal lobe, whereas patients with schizophrenia showed lower activity over the left prefrontal cortex. CONCLUSION: These results, together with biophysical modeling, were then used to discuss the design of future disorder-specific cognitive enhancement interventions based on noninvasive brain stimulation.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".