Neural-network classifiers were used to detect immunological differences in groups of chronic fatigue syndrome (CFS) patients that heretofore had not shown significant differences from controls. In the past linear methods were unable to detect differences between CFS groups and non-CFS control groups in the nonveteran population. An examination of the cluster structure for 29 immunological factors revealed a complex, nonlinear decision surface. Multilayer neural networks showed an over 16% improvement in an n-fold resampling generalization test on unseen data. A sensitivity analysis of the network found differences between groups that are consistent with the hypothesis that CFS symptoms are a consequence of immune system dysregulation. ؉ B-cell compartment and the CD34 ؉ hematopoietic progenitor subpopulation were also detected by the neural network, consistent with the T-cell expansion. Of significant interest was the fact that, of all the cytokines evaluated, the only one to be in the final model was interleukin-4 (IL-4). Seeing an increase in IL-4 suggests a shift to a type 2 cytokine pattern. Such a shift has been hypothesized, but until now convincing evidence to support that hypothesis has been lacking. Chronic fatigue syndrome (CFS) is a medically unexplained illness characterized by over 6 months of fatigue producing a substantial decrease in usual activity and occurring in association with infectious, rheumatological, and neuropsychiatric symptoms. Because CFS often begins acutely with a virus-like presentation, one major idea for its cause is an immune system-activated state, a hypothesis supported by reports of alterations in some activated T-cell populations (9) and by increases in certain subpopulations of "memory" T cells (14). However, other groups have not been able to replicate these findings (15), and we have recently completed a large study in which we were unable to confirm any immune system abnormalities at all in a group of CFS patients (18). One reason for the apparent discrepancy may be statistical. No study besides our own has controlled for multiple comparisons. Moreover, because activity can affect immunological function, we compared our CFS data to data from sedentary healthy people, an effort to match on activity, which had not been done by others. Of interest was the fact that we found immunological differences between Gulf War veterans with CFS and healthy Gulf War veterans. The data suggest that those differences emerged because the veteran population was more immunologically homogeneous than the nonveteran population. Rather than make a type 1 error and reject the immunological hypothesis for CFS in nonveterans based on our analysis, which had employed classical statistical methods, we decided to reevaluate the immunological data from our nonveteran sample using a nonlinear-neural-net method to determine if immunological differences between patients and controls did in fact exist. Neural networks as nonlinear classification schemes are good candidates for this type of medical classification problem and have been shown to be productive in the past in other types of medical classification cases. Previously it has been shown (1, 10) that networks under fairly weak conditions can provide optimal classification for unknown separation surfaces and for unspecified distributions (6). Neural networks work by creating separable partitions of the data at the feature level and then, often, productively combine the results of the linear surfaces into more complex decision surfaces at the next layer, sometimes called the "hidden" layer. The hidden layer of a neural network in fact is critical for its classification performance under nonlinear conditions, and considerable theory and experimental work prescribing the relationship between classification performance and the complexity of the network exists (3, 5). Nonetheless, out-of-sample validity testing is critical for selecting the model and assessing the level of complexity in the data, which is ultimately responsible for the classification performance achieved. In the present study, given the potentially large number of immunologically relevant variables, modular networks, which allow for opportunisitic partitioning of variables into smaller clusters rather than requiring them to be processed at once in parallel as single hidden-layer networks, were tested as possible classifiers. These model classes were compared to perceptron (logit regression) and to combinatorics/single-layer-neural-net classifiers. n out of k bootstraps (2) were used to form a conservative test of the classifier once acceptable results were obtained with a given parametrization of the models. Sensitivity analysis of the classification surface conditioned on each variable was used to estimate the contribution of each immunological factor. Methods (subjects). The subjects were 103 women and 24 men (mean age Ϯ standard deviation [SD], 36.1 Ϯ 8.6 years; 118 Caucasians) who on history, physical examination, and elimination of known medical causes of fatigue by laboratory * Corresponding author. Mailing address:
Hanson, Gause, and Natelson used multilayer neural networks to predict CFS based on 29 immunological genetic transcription factors REF .
Detection of Immunologically Significant Factors for Chronic Fatigue Syndrome Using Neural-Network Classifiers
Clinical and diagnostic laboratory immunology
Clinical and diagnostic laboratory immunology
Medicine Biology