Time estimation: Close your eyes and tell me when a minute goes by

Objective methods for matching neuropsychological patterns: Formulas and comparisons

INTRODUCTION

Objective neuropsychology test score pattern matching methods can help to identify data similarities and differences with comparison groups which can help the clinician in diagnosis and in identifying treatment options.

MATERIALS AND METHODS

The current study examines five methods of matching a data set: Correlation, Configuration, Kullback-Leibler (KL) Divergence, Pooled Effect Size (Cohen's d), and a new method called MNB (Meyers Neuropsychological Battery) Code. Thirty data sets diagnosed with Traumatic Brain Injury (TBI) were compared with four Comparison Group data sets consisting of TBI, Depression, Anxiety and Attention Deficit/Hyperactivity Disorder.

RESULTS

The Correlation Method was correct 90% (27/30) and Configuration was correct 86% (26/30). The KL Divergence was correct 76% (23/30) and the MNB Code was correct 73% (22/30). The Effect Size Method was correct 70% (21/30). When using a simple majority of all the matching methods, the classification rate was 90+ percent.

CONCLUSIONS

The results of this study demonstrate that there are statistical methods that can identify patterns of cognitive strengths and weaknesses. Multiple matching methods and a simple majority of agreement between the different comparisons suggests the best matching profile for diagnosis. In some cases, more than one pattern may be present.

Efficacy of several statistical methods in differentiating TBI and co-occurring conditions: A replication study

OBJECTIVES

(1) Cross-validation of neuropsychological test data sets of moderate-severe TBI (N = 30) with test data from moderate-severe (N = 74); somatization (N = 24) and PCS (N = 22) cases in a database, (2) Determine if cognitive test data sets alone differentiated TBI from other groups, and (3) Evaluate the efficacy of measures in comparisons: Kullback-Leibler, Correlation, Patterns, Cohen's d, and MNB.

MATERIALS AND METHODS

Meyer's Neuropsychological System; Comparison groups -TBI sample with structural evidence of brain injury (CT/MRI); comparison of 5 statistical measures' efficacy in test data analysis comparing a community sample of moderate TBI (N=30) with a data base containing moderate-severe TBI (N = 74) + co-occurring groups (PCS N = 22) + Somatization (N = 24). Measures utilized: Correlation, Kullbeck-Leibler divergence, Cohen's d, MNB code, Configuration.

RESULTS

Combining the five measures most accurately matched the TBI sample (30/30 cases) with MNB comparison groups of similar TBI severity while differentiating those cases from PCS and Somatoform cognitive testdata. Both Kullback Leibler & Cohens' d reduced false positive errors in comparison with the other measures.

Feedback-Based Learning of Timing in Attention-Deficit/Hyperactivity Disorder and Neurofibromatosis Type 1

OBJECTIVE

Patients with Neurofibromatosis Type 1 (NF1) frequently display symptoms resembling those of Attention Deficit/Hyperactivity Disorder (ADHD). Importantly, these disorders are characterised by distinct changes in the dopaminergic system, which plays an important role in timing performance and feedback-based adjustments in timing performance. In a transdiagnostic approach, we examine how far NF1 and ADHD show distinct or comparable profiles of timing performance and feedback-based adjustments in timing.

METHOD

We examined time estimation and learning processes in healthy control children (HC), children with ADHD with predominantly inattentive symptoms and those with NF1 using a feedback-based time estimation paradigm.

RESULTS

Healthy controls consistently responded closer to the correct time window than both patient groups, were less variable in their reaction times and displayed intact learning-based adjustments across time. The patient groups did not differ from each other regarding the number of in-time responses. In ADHD patients, the performance was rather unstable across time. No performance changes could be observed in patients with NF1 across the entire task.

CONCLUSIONS

Children with ADHD and NF1 differ in feedback learning-based adjustments of time estimation processes. ADHD is characterised by behavioural fluctuations during the learning process. These are likely to be associated with inefficiencies in the dopaminergic system. NF1 is characterised by impairments of feedback learning which could be due to various neurotransmitter alterations occurring in addition to deficits in dopamine synthesis. Results show that despite the strong overlap in clinical phenotype and neuropsychological deficits between NF1 and ADHD, the underlying cognitive mechanisms are different.