Sensitive and specific neuropsychological assessments of the behavioral effects of epilepsy and its treatment are essential
Bibliographic record
Abstract
We appreciate the discussion raised by Baxendale and Thompson on the changing role of neuropsychology in view of modern neuroimaging (Baxendale & Thompson, 2010). It is not coincidental that this issue will soon be the subject of an upcoming international meeting in Toronto and that the International League Against Epilepsy (ILAE) assigned a neuropsychology task force to address this topic. In our view, neuroimaging is not a threat to the existence of neuropsychology, but rather a source to stratify clinical neuropsychology and to improve methods and measures. Psychology examines behavior and models the underlying cognitive mechanisms. Most neuroscientists agree that cognition (not behavior) is identical to brain physiology. However, in times of brain-centrism, we should remind ourselves that all clinical practice refers to the patient (i.e., the behavioral level and not the brain level). Neuropsychology provides an unrivalled portfolio of objective, reliable, valid, and cost-effective measures for evaluating multidimensional psychological alterations (performance, well-being/quality of life, and daily activities) caused by brain diseases, transient brain dysfunction, experimental brain manipulations, or any brain-related therapy (drugs, resective and radiosurgery, deep brain and peripheral nerve stimulation, psychotherapy/training). Back when neuroimaging was unavailable, it was considered reasonable to derive as much brain-related information as possible from the neuropsychological data. Today, imaging provides reliable information on structural brain lesions. However, given the complex relationship between brain and behavior one can neither have the complete picture with regard to structure from function nor vice versa [e.g., cognitive impairment in magnetic resonance imaging (MRI)–negative patients]. In a complementary approach, functional neuroimaging reveals task-related brain-activation patterns ranging somewhere between brain and behavior, but it too faces the same complexity. The obtained data may be regarded as a multivariate response from the brain in addition to the recorded behavioral data (e.g., error rates). Functional MRI-based measures will not replace neuropsychological assessments because to provide additional information they must be uncorrelated to overt behavior (e.g. laterality indices indicating hemispheric language shifts in behaviorally inconspicuous patients) (Duncan, 2009). Furthermore, to provide clinically useful information that enriches the neuropsychological assessment, fMRI-based measures must prove their objectivity, reliability, diagnostic/prognostic validity (sensitivity/specificity), and cost-effectiveness. This might be difficult, since brain-activation patterns are strongly affected by factors like task properties, practice/repetition, cognitive capability level, and endocrinologic status (e.g., Fliessbach et al., 2010). In our view, the future lies in the intelligent combination of neuropsychological and psychophysiologic approaches. Neurocognitive studies already refer to established neuropsychological paradigms when defining the experimental tasks; conversely, it is reasonable to relocate (modified parts of the) behavioral testing into the MRI scanner to obtain further information about the addressed functions from the brain level. Given the continuous therapeutic innovations in the treatment of epilepsy (tailored surgical approaches, radiosurgery, deep brain stimulation, vagal nerve stimulation, new drugs/agents, and antiinflammatory treatments), neuropsychologists face ever-increasing demands to scientifically evaluate the behavioral effects of these interventions with sensitive and specific measures. Overlooking amnesia after bilateral amygdalohippocampectomy due to the use of unspecific IQ tests should be a thing of the past (Scoville & Milner, 2000), but overlooking the unexpected cognitive adverse effects of a new drug, topiramate, despite applying a consented battery of cognitive tests provides a more recent example of how difficult this evaluation might be (Aldenkamp et al., 2000). Neuropsychological evaluation comprises behavioral monitoring during the course of a brain-related disease with regard to the underlying dynamic and sometimes progressive pathology. In addition, it allows the follow-up of developmental changes during childhood and older age with regard to the maturing and aging brain in healthy subjects. In conclusion, neuropsychologists must not fear neuroimaging, but rather that medical research relapses to “evaluate” the behavioral effects of diseases and therapies based on unscientific, insensitive, and highly biased “measures” (e.g., global change ratings). In our eyes, the further development of evidence-based brain-related and patient-centered clinical research and practice can simply not afford to ignore neuropsychology. We confirm that we have read the Journal’s position on issues involved in ethical publication and affirm that this report is consistent with those guidelines. Neither of the authors has any conflict of interest to disclose.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".