Keywords schizophrenia - dopamine antagonists - cognitive impairment - cognition - CATIE
Introduction
Neurocognitive impairment is considered one of the core features among patients with
schizophrenia and is attributable to multiple causes. A blockade of dopamine
D2 receptors above approximately 80% with antipsychotics could impair
neurocognitive function, including overall neurocognitive function and vigilance
[1 ]. Antipsychotic drugs have been
associated with mixed results in terms of their effects on neurocognitive impairment
due to the illness [2 ]
[3 ]. The presence of psychotic symptoms has also
been reported to be associated with cognitive impairment. In one longitudinal
follow-up study of patients with first-episode schizophrenia, a decrease in positive
symptoms was related to improvements in neurocognitive functions, including
executive function, spatial memory, concentration/speed, and global cognition [4 ]. Moreover, baseline and current intelligence
quotient (IQ) are reported to affect neurocognitive function in patients with
schizophrenia [5 ]
[6 ]. Thus, neurocognitive impairment in patients
with schizophrenia needs to be comprehensively interpreted from multiple angles. To
the best of our knowledge, no study has investigated the associations of
neurocognitive function, dopamine D2 receptor blockade with
antipsychotics, illness severity, and baseline IQ in a comprehensive manner. The
Clinical Antipsychotic Trials of Intervention Effectiveness (CATIE) [7 ] provides the ideal dataset for such analyses
since its dataset includes a large number of subjects with scores of symptomatology
and neurocognitive functions as well as plasma antipsychotics concentrations that
can be used to estimate dopamine D2 receptor blockade of antipsychotics
in the brain [8 ]
[9 ]
[10 ].
To further highlight this important issue, we re-analyzed the CATIE data using a
machine learning technique to examine if the neurocognitive functions could be
classified based on moderating factors such as dopamine D2 receptor
blockade with antipsychotics, illness severity, and baseline IQ in patients with
schizophrenia.
Methods
Study Population, Assessments, and Study Design
The CATIE trial was funded by the National Institute of Mental Health to compare
the effectiveness of five antipsychotic drugs in patients with schizophrenia
[7 ]. In the present study, we used
data from the CATIE study from subjects who received risperidone, olanzapine, or
ziprasidone treatment. Demographic and study population characteristics are
summarized in [Table 1 ].
Table 1 Demographic and clinical characteristics of the
study sample.
Characteristics
Sample population (N=427)
Total sample (N=573)*
Age, years, mean±SD (range)
41.3±10.6 (18–66)
41.1±10.8 (18–66)
Male, n (%)
317 (74.2)
413 (72.5)
Ethnicity, n (%)
White
262 (61.4)
352 (61.8)
Others
164 (38.4)
218 (38.3)
Duration of education, years, mean±SD (range)
12.3±2.0 (3–21)
12.1±2.2 (3–21)
Duration of treatment, years, mean±SD (range )
16.9±11.1 (0–52)
16.6±11.4 (0–56)
Use of anticholinergics, n (%)
74 (17.3)
95 (16.7)
PANSS, mean±SD (range)
Total score
69.3±18.1 (32–131)
70.2±18 (32–131)
Positive score
16.6±5.6 (7–35)
16.6±5.5 (7–35)
Negative score
18.9±6.4 (7–38)
19.3±6.4 (7–38)
SAS mean score, mean±SD (range)
0.2±0.3 (0–1.8)
0.2±0.3 (0–1.8)
IQ, mean±SD (range)
89.6±18.0 (44–125)
89.7±17.9 (44–125)
Antipsychotics
Risperidone, n (%)
162 (37.9)
214 (37.5)
Trough plasma level, mean±SD (range)
24.9±15.5 (2.8–90.2)
24.9±15.6 (2.8–90.2)
Estimated D2 occupancy, mean±SD (range)
70.4±8.0 (40.9–83.4)
70.4±7.9 (40.9–83.4)
Olanzapine, n (%)
186 (43.6)
246 (43.2)
Trough plasma level, mean±SD (range)
32.2±19.5 (7.0–119.9)
32.2±18.9 (6.6–119.9)
Estimated D2 occupancy, mean±SD (range)
70.5±9.0 (44.9–85.6)
70.6±8.9 (43.8–85.6)
Ziprasidone, n (%)
79 (18.5)
110 (19.3)
Trough plasma level, mean±SD (range)
50.1±35.9 (8.2–228.2)
47.0±33.5 (5.1–228.2)
Estimated D2 occupancy, mean±SD (range)
48.3±13.2 (17.5–77.1)
47.1±13.2 (11.8–77.1
Use of anticholinergics, n (%)
74 (17.3)
95 (16.7)
PANSS, mean±SD (range)
Total score
69.3±18.1 (32–131)
70.2±18 (32–131)
Positive score
16.6±5.6 (7–35)
16.6±5.5 (7–35)
Negative score
18.9±6.4 (7–38)
19.3±6.4 (7–38)
SAS mean score, mean±SD (range)
0.2±0.3 (0–1.8)
0.2±0.3 (0–1.8)
IQ, mean±SD (range)
89.6±18.0 (44–125)
89.7±17.9 (44–125)
Neurocognitive score, mean±SD (range)
Verbal memory (N=426)
0.1±1 (−2.4–2.8)
Processing speed (N=427)
0.1±0.9 (−2.6–3.1)
Working memory (N=427)
0.2±0.9 (−2.8–2.0)
Reasoning (N=427)
0.2±0.9 (−2.4–2.2)
Vigilance (N=389)
0.2±1 (−2.8–3.3)
Neurocognitive summary score (N=427)
0.2±1 (−2.6–2.9)
* IQ: N=535; PANSS: N=526; SAS: N=527; estimated D2
occupancy & trough plasma level: N=528; IQ, intelligence quotient;
PANSS, Positive and Negative Syndrome Scale; SD, standard deviation.
Overall, we included subjects who completed assessments for neurocognitive
function and psychopathology and provided plasma samples for the assessment of
plasma antipsychotic concentrations. The present study sample was chosen due to
previous works that have already established nonlinear mixed-effect models for
this sample [8 ]
[9 ]
[10 ].
The neurocognitive function composite scores, furthermore referred to as domain
scores, were calculated from the z score of the average of the following five
standardized domain scores at month two: verbal memory (the Hopkins Verbal
Learning Test, N=426), vigilance (the Continuous Performance Test, N=389),
processing speed (the Grooved Pegboard, and the Revised Wechsler Adult
Intelligence Scale Digit Symbol Test, N=427), reasoning (the Wisconsin Card
Sorting Test and the Revised Wechsler Intelligence Scale for Children Mazes,
N=427), and working memory (the Letter-number test of auditory working memory
and a computerized test of visuospatial working memory, N=427). In case of
missing values, we have used the available domain scores.
Nine patient-specific moderating factors were selected for further analysis,
which were age, baseline IQ, years of education, Positive and Negative Syndrome
Scale (PANSS) positive score, PANSS negative score, mean Simpson-Angus Scale
(SAS), anticholinergic drug use, drug type (risperidone, ziprasidone or
olanzapine), and estimated minimum dopamine D2 receptor blockade.
Baseline IQ was measured using the Wide Range Achievement Test-third version
(WRAT-3) at baseline in the CATIE trial. PANSS scores were assessed after month
one. Model-predicted trough values of plasma concentrations of antipsychotics
were used to calculate the estimated minimum dopamine D2 receptor
blockade levels on the day of neurocognitive assessment by using a previously
reported model [11 ]. During the CATIE
trial, drug concentrations of risperidone plus 9-hydroxyrisperidone (active
moiety), olanzapine, or ziprasidone were measured at multiple time points.
Plasma antipsychotic concentrations at the trough were calculated on the day of
the neurocognitive assessment using established population pharmacokinetic
models and extracting the Empirical Bayes Estimates for the pharmacokinetics
parameters [12 ]
[13 ]. Thereafter, dopamine D2
receptor blockade levels were estimated by incorporating the predicted plasma
concentration of risperidone plus 9-hydroxyrisperidone, olanzapine, or
ziprasidone in the following one-site binding model:
Blockade (%)=a ×[plasma level/(plasma level+ED50 )] (1)
Here, ‘a’ represents the maximum receptor blockade attributable to the
antipsychotic drug and ‘ED50 ’ is the estimated plasma level of
antipsychotic drug associated with half of the maximum receptor blockade
(Risperidone plus 9-hydroxyrisperidone: a =88.0%,
ED50 =4.9 ng/mL; olanzapine: a =90.7%,
ED50 =7.1 ng/mL; and ziprasidone: a =88.2%,
ED50 =32.9 ng/mL) [11 ]. The
accuracy of these predicted models was previously confirmed with 32 clinically
stable outpatients with schizophrenia, as the mean (95% confidence interval)
prediction errors for the prediction of D2 blockade were 0.64% (−6.18
to 7.46) for risperidone and −1.76% (−5.22 to 1.58) for olanzapine [14 ].
Statistical Methods
Feature selection
Feature selection was performed on the dataset consisting of one continuous
target variable and nine feature variables, where there was no apparent
linear correlation between the features and the outcome. To obtain a
comprehensive perspective on the importance of predictors in our dataset, we
utilized a three-pronged approach for feature selection that compared the
outcomes of genetic algorithm (GA), random forest (RF) feature importance,
and recursive feature elimination (RFE). The GA utilizes a binary string to
represent each potential solution, where each position in the string
corresponds to a predictor variable. The mean squared error (MSE) of an RF
model was utilized to determine the fitness of each individual, taking into
account the predictors represented by the binary string. GA optimization was
executed for 100 generations, with a population size of 50. Convergence of
the GA over time was visualized by plotting the best fitness values across
generations. The second approach involved employing the built-in feature
importance measure of RF. An initial RF model was trained on the complete
dataset, where the feature ranking was determined by the “IncNodePurity”
measure. This measure indicates the overall reduction in node impurity,
measured by the Gini index, from splitting on a variable, averaged over 500
trees. After selecting features with importance values greater than zero,
the final RF model was trained. Our third step involved using the RFE
algorithm, which eliminates features with the least importance in a
sequential manner. RFE was conducted using RF as the base model, with a
repeated cross-validation method. Initially, the complete set of predictors
was employed, and then, each predictor was systematically eliminated one at
a time based on their importance scores. Computation and visualization of
variable importance for the selected features in the final RF models from
each method were undertaken to provide additional insights into the relative
importance of the features. Each feature selection method utilized a 10-fold
cross-validation. The cross-validation was integrated within the feature
selection process in both the GA and RFE. The final model of the RF feature
importance approach was validated using cross-validation to obtain an
unbiased estimate of the model prediction error. By doing so, we mitigate
the risk of overfitting, provide a more unbiased estimate of model
performance, and ensure that our feature selection process was not overly
optimistic towards our training data. Subsequently, an exploratory analysis
excluding two of the main predictors, IQ at baseline and age , from
the data was performed, and each process was repeated using the adapted
dataset. Statistical analyses were performed using R version 4.2.3 [15 ]. The R packages ̒caret̓ and
̒randomForest̓ were used for model training and feature importance, ̒GA̓ was
used for feature selection via a genetic algorithm, ̒Metrics̓ for
calculating MSE, and ̒ggplot2̓ for data visualization.
After conducting feature selection to streamline our predictive variables, we
implemented a Conditional Inference Tree (CTREE) analysis and subsequently
performed a comprehensive performance evaluation to assess the effectiveness
and accuracy of the model. CTREE analysis was performed to construct a
predictive decision tree for the neurocognitive summary score as well as for
the five domain scores based on age, baseline IQ, years of education,
PANSS positive score, PANSS negative score, the mean SAS,
anticholinergic drug use, drug type (risperidone, ziprasidone or
olanzapine), and estimated minimum dopamine D
2
receptor blockade . The normal distribution of these values was
tested by the Kolmogorov-Smirnov test for normality. The linearity between
variables and outcomes was evaluated by producing plots for visual
inspection, which allowed for a direct examination of their relationship
patterns. A correlation matrix was constructed to evaluate multivariate
correlations.
Conditional Inference Tree analysis
CTREE analysis, which represents a non-parametric class of decision tree
analysis, was applied using the ̒caret̓ [16 ] and ̒partykit̓ [17 ]
libraries in R to establish predictive models on our dataset. CTREE is
unbiased in variable selection and capable of handling both numerical and
categorical data. The CTREE algorithm selects cut-offs for decision tree
splits by performing statistical significance tests at each node. It
identifies the most strongly associated feature with the target variable and
uses a permutation test to determine the optimal binary split, adjusting for
multiple testing to avoid overfitting. This process continues recursively,
with splits made based on a significance level set at p<0.05 until no
significant association can be found or other stopping criteria are met.
Specifically, cut-offs for the CTREE models in our analysis were determined
using the ctree2 method in conjunction with the ̒caret̓ package for
hyperparameter tuning, facilitating the selection of optimal parameters for
the decision trees. Our model tuning grid, defined using the ̒expand.grid̓
function, comprised of different depths ranging from 1 to 10 and
mincriterion values ranging from 0.1 to 1. These parameters were applied to
the ̒ctree2̓ method, a variant of CTREE, to train our models. We aimed to
maintain a model that was sufficiently complex to capture the necessary
relationships in the data while remaining interpretable. The CTREE algorithm
aids in this by stopping growth when the addition of another split does not
significantly increase the fit of the model. The robustness of our selected
cut-offs was further assessed through a 10-fold cross-validation approach
during the training phase using the ̒trainControl̓ function from ̒caret̓
[16 ], which helped confirm the
consistency of the results. The reported (optimal) cut-offs were then
selected based on their performance in the cross-validation, with a focus on
achieving the lowest possible MSE and root mean squared error (RMSE),
indicative of the best model fit.
Four distinct models were trained to evaluate the effect of feature selection
methods on the performance of the CTREEs. The first model utilized all
available features in the dataset. The next three models employed different
feature selection methods: GA, RF, and RFE. In the context of predictive
modeling, these strategies are frequently employed to enhance both model
interpretability and performance. To achieve this, the techniques aim to
reduce dimensionality, mitigate overfitting, and improve generalization.
Model performance was assessed using the MSE and RMSE, with lower values of
MSE and RMSE indicating better model fit. Comparing MSE and RMSE values
across different models trained on the same data set can inform about
relative model performances. A model with the lowest MSE or RMSE was
considered the most effective and accurate. Features in the model were
considered statistically significant if the p-value was less than 0.05.
Results
Feature selection
Our findings show a substantial impact of IQ, age, and education years on all
assessed neurocognitive scores across all applied feature selection methods. A
detailed summary of the features selected by each method, including the MSE for
each of the five neurocognitive domains and the neurocognitive summary score, is
given in supplementary Table S3 . Significant variations exist in the
choice of additional variables between the methods. After removing the most
important features, IQ and age, years of education remained the most significant
feature, followed by the PANSS negative score, the estimated dopamine
D2 receptor blockade, and the PANSS positive score. Selected
features of minor importance were the use of anticholinergic comedication, the
type of the administered drug, and the mean SAS score. As expected, the MSE
values, which are indicative of the model's performance, increased when the
features of age and IQ were removed, thus confirming their critical role in
predicting neurocognitive outcomes. GA, in general, showed better performance
(lowest MSE score) when compared to the RF and the RFE approach (i. e., summary
score MSE=0.623 (GA); 0.645 (RFE); 0.646 (RF)).
Conditional Inference Tree analysis
Our analysis yielded valuable insights into the performance of CTREE models. IQ
is consistently presented as a significant factor in nearly all domains and
models. The impact of age was prominent in most domains. Yet, as expected,
education years and the PANSS negative score were the most prevalent without
these two key predictors, emphasizing their potential influence on
neurocognitive scores. Detailed information on model performance (MSE, RSME) and
included features are presented in [Table
2 ]. Also, in this analysis, models based on the GA approach, showed
better performance when compared to the RF and RFE approach, and the models
without previous feature selection. The final tree for the neurocognitive
summary score is presented in [Fig. 1 ].
For each cognitive domain, the included features and final model performances of
the GA-based model were as follows (Roman letters indicating tree node
structure; only nodes p<0.05 reported; number of participants and mean±SD
z-score for each node in brackets):
Fig. 1 Decision tree summarizing the importance of various
features on the neurocognitive summary score (N=427) including number of
patients, decision criterion and mean±standard deviation z-score for
each node.
Table 2 An overview of CTREE analysis
results.
Neurocognitive score
Method
Included features (p<0.05)
MSE
RMSE
Included features without IQ and age (p<0.05)
MSE
RMSE
Verbal memory
–
IQ
0.95578
0.97572
Education years, PANSS negative
0.96967
0.97963
GA
IQ
0.96115
0.97712
–
–
–
RFE
IQ
0.95358
0.97330
Education years
0.96784
0.98077
RF
IQ
0.94401
0.96651
Education years
0.98277
0.98889
Processing speed
–
IQ, age, education years, PANSS negative
0.74602
0.86022
Education years, PANSS negative
0.81257
0.89785
GA
IQ, age
0.71158
0.83753
Education years, PANSS negative
0.81843
0.89757
RFE
IQ, age, education years, PANSS negative
0.72371
0.84485
Education years, PANSS negative
0.80783
0.89257
RF
IQ, age, education years
0.70018
0.82931
Education years, PANSS negative
0.81342
0.89790
Working memory
–
IQ, age
0.71445
0.84385
Education years
0.79657
0.89079
GA
IQ, age, PANSS negative
0.70179
0.83417
–
–
–
RFE
IQ, age, PANSS negative
0.72460
0.84549
Education years, anticholinergic comedication
0.77356
0.87637
RF
IQ, age, PANSS negative
0.71153
0.83486
Education years
0.79500
0.88817
Reasoning
–
Age, IQ
0.78274
0.88234
No model
–
–
GA
Age, IQ
0.73589
0.85346
–
–
–
RFE
Age, IQ
0.75616
0.86710
Education years
0.87050
0.92966
RF
Age, IQ
0.75232
0.86181
Education years
0.86284
0.92735
Vigilance
–
IQ, age
0.89557
0.94169
Education years
0.94561
0.96882
GA
IQ, age
0.84388
0.91316
–
–
–
RFE
IQ, age
0.88846
0.93566
Education years
0.94823
0.96912
RF
IQ, age
0.86142
0.91974
Education years
0.94684
0.96777
Neurocognitive summary score
–
IQ, age, education years
0.740366
0.85965
Education years
0.86634
0.92461
GA
IQ, age, education years, anticholinergic comedication
0.67609
0.81961
–
–
–
RFE
IQ, age
0.71209
0.84267
Education years
0.867101
0.92890
RF
IQ, age, education years
0.71670
0.84232
Education years
0.86900
0.92921
CTREE, Conditional Inference Tree; GA; genetic algorithm, OQ,
intelligence quotient; MSE, mean squared error; RMSE, root mean squared
error; RF; random forest to show feature importance, RFE; random forest
feature elimination.
Verbal memory (N=426, MSE=0.96115 and RMSE=0.97712):
Processing speed (N=427, MSE=0.71158 and RMSE=0.83753):
I.
IQ≤95 (N=235; −0.234±0.902) vs. IQ>95 (N=192; 0.433±0.865),
p<0.001
Age≤41 (N=98; 0.116±0.784) vs. IQ>41 (N=137; −0.484±0.899),
p<0.001
Age≤47 (N=137; 0.650±0.831) vs. age>47 (N=55; −0.110±0.695),
p<0.001
Working memory (N=427, MSE=0.70179 and RMSE=0.83417):
I.
IQ≤94 (N=234; −0.140±0.888) vs. IQ>94 (N=193; 0.543±0.807),
p<0.001
Age≤52 (N=203; −0.030±0.845) vs. age>52 (N=31; −0.856±0.839),
p<0.001
Reasoning (N=427, MSE=0.73589 and RMSE=0.85346):
I.
Age≤41 (N=195; 0.535±0.800) vs. age>41 (N=232; −0.126±0.939),
p<0.001
IQ≤105 (N=154; 0.387±0.790) vs. IQ>105 (N=41; 1.092±0.563),
p<0.001
IQ≤88 (N=111; −0.443±0.909) vs. IQ>88 (N=121; 0.164±0.874),
p<0.001
IQ≤64 (N=20; −0.124±0.937) vs. IQ>64 (N=134; 0.464±0.739),
p=0.042
Age≤54 (N=92; −0.314±0.830) vs. age>54 (N=19; −1.064±1.037),
p=0.029
Vigilance (N=389, MSE=0.84388 and RMSE=0.91316):
I.
IQ≤90 (N=181; −0.139±0.899) vs. IQ>90 (N=208; 0.488±0.974),
p<0.001
Age≤52 (N=158; −0.014±0.868) vs. age>52 (N=23; −0.994±0.603),
p=0.007
Neurocognitive Summary Score (N=427, MSE=0.67609 and RMSE=0.81961):
I.
IQ≤95 (N=235; −0.196±0.901) vs. IQ>95 (N=192; 0.637±0.855),
p<0.001
Age≤52 (N=204; −0.06±0.845) vs. age>52 (N=31; −1.089±0.736),
p<0.001
Age≤47 (N=137; 0.821±0.792) vs. age>47 (N=55; 0.178±0.839),
p<0.001
IQ≤57 (N=23; −0.656±0.765) vs. IQ>57 (N=181; 0.016±0.827), p=0.003
Education years≤13 (N=108; 0.727±0.764) vs. education years>13 (N=29;
1.172±0.807), p=0.018
Anticholinergic comedication yes (N=31; −0.353±0.847) vs. no (N=150;
0.092±0.804), p=0.044
Discussion
Our CTREE analysis, guided by GA or RFE feature selection or by RF feature
importance, revealed interesting findings regarding the influence of moderating
factors on various neurocognitive domains. IQ represents the most important
predictor among all neurocognitive domains. In this sample, the mean IQ was 90±18,
ranging from 44–125. Decision trees based on GA models suggested diverse cut-offs
for the IQ ranging from 64 to 105. Four models suggested a quite consistent
threshold of 94/95 for moderating cognitive scores. Patients with a baseline IQ
below 94/95 were, in general, predicted to have significantly lower z-scores in the
verbal memory, processing speed, and working memory domain. This also holds true in
the model for the neurocognitive summary score.
The age of patients played a role in all neurocognitive domains except for
verbal memory, where other factors (IQ and years of education) were more important.
The mean patient age was 41.1±10.8 years, ranging from 18 to 66 years. The decision
tree models revealed several cut-offs, ranging from 41 to 54 years. An age above 52
years was suggested to be predictive for significantly lower z-scores in the working
memory domain and the vigilance domain, as well as for the results of the
neurocognitive summary score (based on GA models).
While being one of the main predictors of neurocognitive domain scores, the years
of education have not been found predominant in the primary GA models (see
supplementary Figure S4 for details). However, after the exclusion of age
and IQ, education years was represented in all final models independent from the
method used. While the number of education years fluctuated widely among the patient
sample from 3 to 21 years (mean 12.1±2.2 years), the cut-off among all models
consistently found a minimum number of 12/13 years being relevant in terms of better
cognitive performance.
However, other factors, predominantly a higher severity of PANSS negative
symptoms , seem to cancel out these effects, resulting in significantly lower
z-scores (different models processing speed, verbal memory, working memory domains
(data not shown)). For the working memory domain, two models (GA and RFE) identify
the feature as significant. Here, increased negative symptom scores are associated
with lower cognitive performance when compared to the patient group with the same IQ
(>94) but a negative symptom score above 30 (p=0.042). In the processing speed
domain, the PANSS negative score becomes significant (p<0.05) in all models after
removing age and IQ from the equation, thus suggesting an interplay between these
variables. Patients with the same level of education (>12 years) but a negative
symptom score above 26 showed, in general, a lower cognitive performance compared to
the patient group with a score below 26 or equal.
Drug type, mean SAS score, and PANSS positive score were, in general, not
found to represent strong moderators for neurocognitive functioning, according to
feature importance ranking and the final models.
The concomitant use of anticholinergic medication was represented as a
significant feature in the neurocognitive summary score GA-based model (see [Fig. 1 ]), with the use of this drug class
being in general predictive for lower z-scores in 31 patients. However, this finding
must be regarded in terms of the complex interactions presented in the models.
Our findings furthermore underline the nuanced role of the estimated minimum
dopamine D
2
receptor blockade on neurocognition as
presented in a previous analysis of the same patient sample [1 ]. The feature of estimated D2
occupancy was frequently selected/ranked among the significant features across all
methods in the vigilance, reasoning, working memory, and verbal memory domains and
the neurocognitive sum score. It was further included in the final model for the
processing speed domain alongside IQ, age, education years, and PANSS negative score
when no prior feature selection was applied, with an estimated D2
occupancy threshold above 77.6% indicating poorer performance. However, it did not
become significant (p=0.22). This threshold would be well in line with the findings
from previous studies on olanzapine that indicate that D2 occupancies of
around 80% are related to maximum attainable therapeutic effects (measured by
schizophrenia symptom scales) [18 ]. Of note,
D2 occupancy showed a moderate correlation with the type of
antipsychotic being used for the treatment in the patient cohort.
To sum up, in contrast to the findings from Sakurai and colleagues [1 ], the role of estimated D2
occupancy was rather negligible among the model predictions and might rather be
mediated by other factors. Thus, the role of D2 occupancy on
neurocognitive performance remains elusive, pointing to the need for further study
to disentangle its effects. Besides the dominant factors, age and IQ, the PANSS
negative scores appear to broadly impact multiple cognitive domains. In everyday
patient care, variables such as IQ, age, and years of education remain rather
unmodifiable. Negative symptoms, however, offer an interesting area for potential
treatment interventions. Previous studies have demonstrated a strong link between
negative symptoms and impaired cognitive function in patients with schizophrenia
[19 ]. The focus of treatment that targets
negative symptoms may not only ameliorate these symptoms but may also lead to
improvements in cognitive function. Several studies have found that interventions
targeting negative symptoms, i. e., cognitive remediation therapy and social skills
training, can positively affect cognitive abilities [20 ]
[21 ]. Furthermore, clinical
focus on negative symptoms to enhance cognitive function in schizophrenia is of
major importance since cognitive impairments are also strongly linked to functional
outcomes, such as employment and social interaction [22 ]. Thus, by better management of negative symptoms, clinicians may open
a pathway to enhance the overall real-world performance and functional recovery of
patients with schizophrenia [22 ]
[23 ].
Several limitations should be taken into account when interpreting the findings of
this study and considering their broader applicability. First, the study relies on
specific statistical techniques and models such as GA, RF, and RFE, which could lead
to biases depending on underlying assumptions and parameter tuning. The chosen
feature selection methods, while comprehensive, might have overlooked interactive or
nonlinear effects between variables, potentially limiting the interpretation of
certain predictors like the estimated D2 occupancy. Second, we have
chosen not to set aside a separate holdout set for external validation; all data is
used for both training and validation. We relied on 10-fold cross-validation to
provide a robust estimate of the model’s performance, avoiding any reduction in
training data. The potential for over-optimistic results still exists, and the lack
of a separate, independent validation sample may affect the true estimation of the
model’s predictive power. Furthermore, we refrained from using a weighting or
enrichment scheme to maintain the integrity of the distribution in our standardized
outcome data, manage the risk of overfitting, and ensure a clear interpretability of
the model. Third, since the effects of dopamine D2 blockage by
antipsychotic medication on neurocognition can not only be altered by changes in age
and IQ; problems may also occur, with negative symptoms being a strong predictor for
the outcome domains. Negative symptoms are highly complex and may arise from various
underlying neural deficits. No single receptor or pathway can be clearly pinpointed
as the mediator of negative symptoms. Moreover, the impact of antipsychotic
medications on negative symptoms may be indirect, arising from improvements in other
symptom domains, or from interactions between various neurotransmitter systems with,
i. e., serotonin 5-HT2A antagonism being repeatedly highlighted in these
terms. Whereas olanzapine [24 ] and risperidone
[25 ] predominantly exert their
antipsychotic action via D2 and 5-HT2A receptors, ziprasidone
also has activity at 5-HT1A receptors [26 ], which may contribute to its clinical effects on negative symptoms.
Fourth, the exclusion of key predictors like IQ and age in the secondary analyses
might raise questions about the validity of the models in generalizing to broader
populations. The study's focus on specific neurocognitive scores and certain
medications (e. g., risperidone, ziprasidone, or olanzapine) may limit applicability
to other clinical settings or cognitive functions. Lastly, our study establishes
associative links rather than causal connections, and these findings prompt further
research to investigate the potential for causality.
Conclusion
The work presented shows a robust approach, employing various feature selection
methods and CTREE analyses to proficiently exhibit the clinical relevance of
particular factors, primarily IQ, age, the number of education years, and the
severity of negative schizophrenia symptoms (assessed by PANSS negative score)
across multiple cognitive domains. Overall, smaller z scores, which indicate lower
neurocognitive function, were associated with advanced age (i. e., age above 52
years), lower IQ (i. e., IQ below 94/95), lower number of education years (i. e.,
less than 12/13 school years), and more severe negative symptoms (i. e., PANSS
negative score above 26). While the verbal memory, processing speed, reasoning, and
vigilance domains were dominated by age and IQ as the most relevant factors, the
working memory and neurocognitive summary domains seem to be attributable to a
highly multifaceted interplay of influencing factors. Our findings confirm a strong
connection between negative symptoms and impaired cognitive function, as discussed
in previous studies [19 ]
[22 ]
[23 ].
Personalized treatment plans might benefit from a focus on better management of
negative symptoms to enhance real-world performance and functional recovery in
schizophrenia patients. Of note, the presented results offer insights into the
predictive importance of clinical factors on neurocognitive scores but do not infer
causality. Further investigations are warranted to understand the nature of these
relationships fully.
Data availability statement
Data availability statement
The original contributions can be directed to the corresponding author by reasonable
request.
Author Contributions
XML developed the first draft of the protocol and performed the analysis. YM provided
assistance with machine learning analysis. RBB conducted the pharmacokinetic
analysis. HU supervised the entire manuscript writing and contributed to the
revision of the protocol. All authors have read and approved the final
manuscript.
