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ARTICLE OPEN
Baseline symptom-related white matter tracts predict
individualized treatment response to 12-week antipsychotic
monotherapies in first-episode schizophrenia
Ying Chen
1,2
, Shanming Liu
3
, Bo Zhang
3
, Gaofeng Zhao
4
, Zhuoqiu Zhang
3
, Shuiying Li
3
, Haiming Li
3
, Xin Yu
5
, Hong Deng
2,3
✉and
Hengyi Cao
6,7
© The Author(s) 2024
There is significant heterogeneity in individual responses to antipsychotic drugs, but there is no reliable predictor of antipsychotics
response in first-episode psychosis. This study aimed to investigate whether psychotic symptom-related alterations in fractional
anisotropy (FA) and mean diffusivity (MD) of white matter (WM) at the early stage of the disorder may aid in the individualized
prediction of drug response. Sixty-eight first-episode patients underwent baseline structural MRI scans and were subsequently
randomized to receive a single atypical antipsychotic throughout the first 12 weeks. Clinical symptoms were evaluated using the
eight “core symptoms”selected from the Positive and Negative Syndrome Scale (PANSS-8). Follow-up assessments were conducted
at the 4th, 8th, and 12th weeks by trained psychiatrists. LASSO regression model and cross-validation were conducted to examine
the performance of baseline symptom-related alterations FA and MD of WM in the prediction of individualized treatment outcome.
Fifty patients completed both clinical follow-up assessments by the 8th and 12th weeks. 30 patients were classified as responders,
and 20 patients were classified as nonresponders. At baseline, the altered diffusion properties of fiber tracts in the anterior thalamic
radiation, corticospinal tract, callosum forceps minor, longitudinal fasciculi (ILF), inferior frontal-occipital fasciculi (IFOF) and superior
longitudinal fasciculus (SLF) were related to the severity of symptoms. These abnormal fiber tracts, especially the ILF, IFOF, and SLF,
significantly predicted the response to antipsychotic treatment at the individual level (AUC =0.828, P< 0.001). These findings
demonstrate that early microstructural WM changes contribute to the pathophysiology of psychosis and may serve as meaningful
individualized predictors of response to antipsychotics.
Translational Psychiatry (2024) 14:23 ; https://doi.org/10.1038/s41398-023-02714-w
INTRODUCTION
In schizophrenia, only ~70% of affected individuals respond to
antipsychotic drug treatment, and even fewer go into remission
[1,2]. Pre-treatment prediction of the subsequent response could
reduce the time spent on ineffective treatments, shorten patient
suffering, and reduce possible mortality [3,4]. The ability to
identify brain biomarkers of antipsychotic nonresponders using
magnetic resonance imaging may lead to improved prognosis and
the detection of malleable central nervous system targets for the
development of new treatment strategies.
Second-generation antipsychotics (SGAs) are widely believed to
work by decreasing striatal dopamine via dopamine receptor
blockage within the mesolimbic pathway to alleviate positive
symptoms and increasing cortical dopamine via 5-HT(2A) antag-
onism in presynaptic neurons within the mesocortial pathway to
improve negative symptoms [5,6]. These neurotransmitters
modulate synapses at glutamate (N-methyl-d-aspartate [NMDA])
receptors that are involved in synaptic plasticity and may cause
delayed corollary discharges [7]. Animal studies noted that
antipsychotic treatment following administration of the copper
chelator cuprizone promoted oligodendrocyte development and
remyelination [8,9]. Thus, the integrity of WM may be a sensitive
index of the brain pharmacological mechanism of action of SGA.
Diffusion tensor imaging (DTI) is widely used to evaluate the
structural integrity of WM, and voxelwise metrics such as fractional
anisotropy (FA) and mean diffusivity (MD) are generally considered
sensitive measures for axonal/myelin damage [10–12]. A handful of
DTI studies have documented the association between FA/MD of
WM and response to treatment [13–15]. Further, inconsistencies prior
work may be related to differences in clinical variables such as the
illness course/chronicity, substance misuse, and previous exposure to
antipsychotics. Therefore, investigating patients with drug-naive first-
episode psychosis may address to disentangle which brain white
matter changes may predict response to treatment.
To date, only three studies have investigated the effects of
antipsychotic use on WM tracts in drug-naive first-episode
Received: 8 February 2023 Revised: 6 December 2023 Accepted: 13 December 2023
1
Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, China.
2
Hope Recovery and Rehabilitation Center, West
China Hospital of Sichuan University, Chengdu, China.
3
Mental Health Center, West China Hospital of Sichuan University, Chengdu, China.
4
Shandong Daizhuang Hospital, Jining,
Shangdong, China.
5
Peking University Sixth Hospital, Peking University Institute of Mental Health, NHC Key Laboratory of Mental Health (Peking University), National Clinical
Research Center for Mental Disorders (Peking University Sixth Hospital), Beijing, China.
6
Center for Psychiatric Neuroscience, Feinstein Institute for Medical Research, Manhasset,
NY, USA.
7
Division of Psychiatry Research, Zucker Hillside Hospital, Glen Oaks, NY, USA. ✉email: rhdeng88@hotmail.com
www.nature.com/tp
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schizophrenia. These studies show that antipsychotic medications
appear to alter or improve FA or MD of WM, such as in the bilateral
anterior cingulate gyrus (ACG), corticospinal tract (CT), anterior
thalamic radiation (ATR), longitudinal fasciculi (ILF), inferior fronto-
occipital fasciculi (IFOF), and uncinated fasciculus (UF) structural
abnormalities, especially at remission [16–18]. However, while
these studies typically report group-level WH tract differences
between psychosis before and after antipsychotic administration,
whether these findings have predictive value for individualized
drug responses is unclear.
WH alterations may occur before the onset of psychosis, and
FA/MD of WM changes have been reported in subjects with a high
risk of psychosis [19,20]. The severity of WM alterations such as
fronto-temporal, fronto-occipital, and fronto-striatal WH at onset
were associated with the severity and persistence of the signs and
symptoms of schizophrenia, suggesting that baseline WM
alterations may serve as an early marker for differentially
characterizing patients with poor or good outcomes [21–23].
Furthermore, the cortex, especially the frontal lobe, is the core
component of the dopamine projection system [24,25]. However,
it is still unknown whether psychotic symptom-related alterations
in FA and MD of WM at the early stage of the disorder may
provide aid to individualized prediction of drug response.
In this study, we investigated the above questions using DTI data
acquired from first-episode schizophrenia patients with no prior
medication. Patients underwent baseline structural MRI scans and
were subsequently randomized to receive a single atypical
antipsychotic throughout the first 12 weeks. It was hypothesized
that the altered FA/MD of WM was related to the severity of
psychotic symptoms at baseline, and those changes would show
potential as individualized predictive biomarkers of response to SGA.
METHODS
Participants
A total of 68 drug-naive patients between 18 and 45 years old were
diagnosed with schizophrenia based on the Structured Clinical Interview
for the DSM-IV Axis I disorder and had no previous psychiatric treatment.
Patients underwent MRI scans and symptom ratings before assigned to a
randomized open-label treatment with risperidone, olanzapine or aripi-
prazole for up to 1 year (Clinical trials.gov ID: NCT01057849). Clinical
symptoms were evaluated using the eight “core symptoms”selected from
the Positive and Negative Syndrome Scale (PANSS-8), which has more
acceptable internal consistency and comparable sensitivity to early
improvement in psychotic symptoms than the PANSS-30 [26]. This analysis
included data only from the first 12 weeks of treatment. During this period,
patients received a single antipsychotic that started with low dosage and
gradually increased to a standard therapeutic range (3–6 mg risperidone,
15–30 mg aripiprazole, or 10–25 mg olanzapine per day) in 2 weeks.
Follow-up assessments were conducted at the 4th, 8th, and 12th weeks by
trained psychiatrists. To ensure the consistency and reliability of ratings
across the study, three psychiatrists with more than 5 years of experience
in clinical psychiatry attended a 1-week training workshop on the use of
the rating instruments prior to the study. After training, they achieved an
interrater reliability of 0.80 for the PANSS-8 score.
Evaluation of treatment response
Treatment response was operationalized as a reduction in symptom
severity to the levels required by the remission criteria of the
Schizophrenia Working Group Consensus [27]. According to these criteria,
clinical improvement is reached when a simultaneous rating of mild or less
(equivalent to 1, 2, or 3) is given in all the following items of the PANSS-8:
delusions (P1), conceptual disorganization (P2), hallucinatory behavior (P3),
mannerisms and posturing (G5), unusual thought content (G9), blunted
affect (N1), social withdrawal (N4), and lack of spontaneity and flow of
conversation (N6). The clinical recommendation is that antipsychotic
treatment with a specific drug should be continued for 6–8 weeks before
switching to a different medication owing to lack of efficacy or adverse
effects. Hence, in this study, we defined treatment response as meeting
the remission criteria at the 8th or 12th week. Only Fifty patients
completed both clinical follow-up assessments at the 8th and 12th weeks
and were therefore included in this study as the final sample. The drop-out
participants did not differ from the rest of the sample in characteristics and
symptom severity (Supplementary Table S1).
MRI data acquisition
The MRI scans were performed before medication using a GE Signa EXCITE
3.0-T scanner (GE Healthcare, Milwaukee, Wisconsin) equipped with an
8-channel phase array head coil. The DTI data were acquired using a
bipolar diffusion-weighted spin‒echo planar imaging (EPI) sequence
(TR =10000 ms, TE =70 ms) with a 128 × 128 matrix over a field of view
of 240 × 240 mm and 42 axial slices of 3 mm thickness to cover the whole
brain without gap. Each DTI dataset included 20 images of unique diffusion
directions (B =1000) and a nondiffusion image (B =0). High-resolution T1
data were acquired using a 3D spoiled gradient (3D-SPGR) sequence:
TR =8.5 ms, TE =3.5 ms, TI =400 ms, flip angle =12, 240*240 matrix over
afield of view of 240*240 mm, and 156 axial slices of 1 mm thickness. All
scans were reviewed by an experienced neuroradiologist to exclude gross
brain abnormalities.
Imaging processing
The routine DTI preprocessing included head motion and eddy current
correction, brain extraction, and tensor model fitting was performed using
FSL (FMRIB Software Library, http://www.fmrib.ox.ac.uk/fsl). We used
automated fiber quantification software (AFQ) to identify 20 white matter
traces in individual subjects. The identification procedure included three
primary steps: whole-brain deterministic fiber tractography, waypoint ROI-
based tract segmentation, and probability map-based fiber refinement
using the 20-tract Johns Hopkins University white matter template. The 20
identified tracts were the left and right ATR, cingulum–cingulate (CC),
cingulum–hippocampus pathway, inferior fronto-occipital fasciculus (IFOF),
inferior longitudinal fasciculus (ILF), superior longitudinal fasciculus (SLF),
uncinated fasciculus and arcuate fasciculus, and the forceps major of the
splenium and the forceps minor of the genu of the corpus callosum. After
tract identification, we smoothed each tract using a 10-point moving
average filter to reduce local variation caused by imaging noise. The
diffusion measurements along the tract core, defined as the tract profile,
were extracted from each fiber tract, including the FA and MD values.
Hence, each tract had two features, and each subject had 40 features to
depict their global white matter status.
Diffusion properties and clinical associations
To confirm the correlation between diffusion properties and symptoms at
baseline, partial Pearson correlation was performed to examine relations
between 40 white matter features and PANSS-8 scores at baseline, with
age, gender, and duration of untreated psychosis as covariates. To adjust
the significant values for multiple comparisons, we used the
Benjamini–Hochberg false discovery rate (FDR qvalue selected to maintain
the false positive error rate <0.05).
Prediction of treatment outcome with diffusion properties
We next sought to investigate whether baseline FA and MD of WM would
be capable of distinguishing antipsychotic responders from nonresponders
at the individual level. To this end, we trained a cross-validated generalized
LASSO regression model with treatment outcome as the dependent
variable and baseline diffusion properties as predictors. To constrain the
number of features in the model and meanwhile include all features
relevant to the disorder, we preselected the FA and MD measures for
model training. Here, only measures significantly associated with baseline
symptoms at uncorrected P< 0.05 were included in the model as input
predictors. We also trained the model with all baseline diffusion properties
as predictors as a supplementary analysis (see Supplementary Materials).
The LASSO regression is an L1-norm regularization method that
incorporates a shrinkage penalty term λto avoid model overfitting, which
coerces the coefficients of some less important predictors to be shrunken
to zero. Specifically, the predictors included in the model were adjusted for
age, sex, antipsychotic drug dosage, duration of untreated psychosis, and
PANSS-8 scores at baseline. Similar to our prior work [28–30], a repeated
nested cross-validation (CV) method (10 outer folds, each with 10 inter
folds) was used in which the tuning parameter λwas optimized within the
inner cycles and subsequently utilized to predict remaining subjects in the
outer cycles. This procedure eventually yielded predicted probabilities of
nonresponders for each individual in the main dataset, based on which the
classification accuracy was calculated. To ensure the robustness of the
Y. Chen et al.
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results, we repeated the CV 100 times, each time by randomly parcellating
the sample. The final classification performance was determined as the
average area under curve (AUC) of the receiver operating characteristic
(ROC) curves from the 100 runs, and the significance of the performance
was determined by 1000 permutations. We also investigated whether the
top features selected by the model (at least 8 out of 10 cycles) would be
capable of predicting individualized symptom changes in first-episode
schizophrenia as a supplementary analysis (see Supplementary Materials).
RESULTS
12-Week treatment outcome
By the end of the 12th week, 30 patients met the remission criteria
as responders, and 20 patients were classified as nonresponders.
Demographic and clinical variables at baseline were not signifi-
cantly different between responders and nonresponders (Table 1).
Diffusion properties and clinical associations at baseline
In partial correlation analysis between PANSS-8 scores and FA/MD
of each fiber tract, we found positive correlations between PANSS-
8 scores and average MD of the following fiber tracts after FDR
correction: left and right IFOF (r=0.564, q=0.002; r=0.456,
q=0.013), and left and right ILF (r=0.493, q=0.009; r=0.425,
q=0.03) (Fig. 1). At a more liberal threshold without FDR
correction, PANSS-8 score was significantly correlated with the
FAs of the left ILF (r=-0.296, P=0.044) and right ILF (r=−0.374,
P=0.01), as well as MDs of the left ATR (r=0.358, P=0.013), left
corticospinal (r=0.362, P=0.012), right corticospinal (r=0.389,
P=0.007), genu of corpus callosum (r=0.346, P=0.017), and
right SLF (r=0.376, P=0.009). Therefore, these eleven measures
(two FA measures and nine MD measures) were subsequently
used as predictors in the LASSO regression model.
Table 1. Clinical and demographic information for the first-episode schizophrenia patients.
Characteristic Responders, N=30 Nonresponders, N=20 t/F P
Age (years) 25.13 ± 7.45 25.65 ± 7.79 −0.24 0.815
Sex (M/F) 13/17 7/13 0.35 0.556
Duration of untreated psychosis (months) 5.73 ± 4.34 5.65 ± 4.63 0.07 0.949
Baseline PANSS-8 24.8 ± 5.85 23.65 ± 6.12 0.67 0.507
Antipsychotic type (risperidone/aripiprazole/olanzapine) 16/6/8 6/8/6 3.33 0.074
Mean antipsychotic dosage (olanzapine equivalents, mg/day) 19.08 ± 4.26 19.03 ± 4.87 0.34 0.564
Fig. 1 Relationship between diffusion properties and the severity of core symptoms at baseline in first-episode schizophrenia. A Fiber
tracts significantly associated with symptoms. BThe red lines indicate MD of fiber tracts were positively correlated with PANSS-8 scores. IFOF
inferior fronto-occipital fasciculi, ILF longitudinal fasciculi, MD mean diffusivity.
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Classification performance of treatment response
The average AUC from the 100 repeats of the LASSO regression
model was 0.828 (range: 0.81–0.86) (P< 0.001, average sensitiv-
ity =0.867 and average specificity =0.636). Here, the FA of the
right ILF and MDs of the left IFOF and the right SLF had nonzero
coefficients at least 8 out of 10 cycles during all 100 repeats, and
were therefore selected as final features. The post-hoc ttest
revealed significantly higher FA of the right ILF (t=5.69, P< 0.001)
but lower MDs of the left IFOF and the right SLF in responders
compared with nonresponders (t=−2.25, P=0.029; t=−2.62,
P=0.012) (Table 2and Fig. 2).
DISCUSSION
Here, we provide evidence for a baseline psychotic symptom-
related white matter tract biomarker that potentially predicts
response to antipsychotic treatment in first-episode schizophrenia.
Importantly, the study sample was treatment-naive to ensure that
the findings were not confounded by the drug. Two main findings
emerged from this study. First, the altered diffusion properties (FA
or MD) of fiber tracts in the ATR, corticospinal tract, callosum
forceps minor, IFOF, ILF and SLF were related to the severity of
symptoms, demonstrating that early microstructural WH changes
contribute to the pathophysiology of psychosis. Second, these
abnormal fiber tracts, especially the ILF, IFOF, and SLF, significantly
predicted the response to antipsychotic treatment at the
individual level. This suggests that these symptom-related WH
changes could be an outcome marker after the onset of psychosis
or even a target for intervention and preventive strategies.
Our findings decreased FA and increased MD of several fiber
bundles throughout the brain correlated with core positive and
negative symptom severities consistent with a “disconnection”
hypothesis of symptoms in schizophrenia [31,32]. Several meta-
analytic studies have investigated the role of WH irregularities in
schizophrenia spectrum disorders [33–36]. A meta-analysis of WM
alteration in patients with FES indicated widespread abnormalities
across white matter tracts, with evidence for reductions in FA in the
corpus callosum, the left ILF and IFOF [34]. Even in chronic
schizophrenia, the meta-analysis of 15 DTI studies also observed
significant FA reductions in the genu and splenium of corpus
callosum, the left anterior thalamic radiation, the left IFOF and ILF
[36]. Moreover, the relationship between aberrant FA of these WM
Table 2. Significant features for discriminating treatment responders and nonresponders.
Selection times Hemisphere Label Feature type Responders Nonresponders tP
10 Right ILF FA 0.45 ± 0.2 0.42 ± 0.02 5.69 <0.001
9 Left IFOF MD 0.75 ± 0.02 0.77 ± 0.03 −2.25 0.029
8 Right SLF MD 0.68 ± 0.02 0.7 ± 0.03 −2.62 0.012
ILF longitudinal fasciculi, IFOF inferior fronto-occipital fasciculi, SLF superior longitudinal fasciculus, FA fractional anisotropy, MD mean diffusivity.
Fig. 2 Diffusion properties in discriminating nonresponders from responders in first-episode schizophrenia after 12-week antipsychotic
monotherapies. A The selected diffusion properties showing highest predictability for nonresponders from cross-validated LASSO regression
in first-episode schizophrenia. Red and blue indicated increased and decreased fractional anisotropy, respectively. BThe receiver operating
characteristic (ROC) curve for nonresponders. ILF longitudinal fasciculi, IFOF inferior fronto-occipital fasciculi, SLF superior longitudinal
fasciculus.
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tracts and psychotic symptoms of schizophrenia were reported
among previous studies [22,37]. Consistent with this study, several
studies found the inverse relationship between FA of SLF, ILF, and
IFOF and negative symptoms and auditory verbal hallucinations in
schizophrenia [37–39]. Taken together, these implied that white
matter dysintegrity may represent a “trait”marker, related to the
underlying pathophysiology in schizophrenia.
Beyond the group level for white matter correlated with
symptoms at baseline, our longitudinal follow-up study also provided
evidence that these baseline psychotic symptom-related FA and MD
of WM may serve as an individualized predictor for antipsychotic
treatment response in patients with schizophrenia. Similar to our
findings, previous group-level analysis studies found more wide-
spread FA decreases at baseline in FEP patients with a subsequent
poorer response, and that baseline global white matter network
organization showed greater alterations in FEP patients who
subsequently showed a poorer treatment response [40–42]. Taken
together, all these studies highlight the usefulness of baseline WM
integrity in predicting response to treatment. Furthermore, the
LASSO regression model in this study correctly classified 82.8% of
patients as responsive, which may represent an important pre-
liminary step to provide clinicians with decision support in selecting
the ideal antipsychotic treatment for schizophrenia in a personalized
manner. Further studies using larger and independent samples are
required to replicate these findings.
We focused on predicting biomarkers of symptom-related FA
and MD in WM. Longitudinal studies have observed that
longitudinal increases in FA values, especially in the IFOF, ILF,
SLF, and anterior thalamic radiation, are significantly correlated
with improved symptoms at follow-up [17,18,43]. Consistent with
these findings, the top treatment response predicting features in
our study were located in the ILF, IFOF, and SLF. These WM tracts
connect frontal, temporal, parietal and occipital areas, which have
been implicated in several cognitive functions, such as visuospa-
tial processing, emotional regulation, memory and language, in
schizophrenia [44,45]. In addition, altered FAs in the SLF and IFOF
have been found to be a biomarker for auditory hallucinations,
and FA in the ILF has been linked to positive symptoms [46,47].
Furthermore, these regions are major target of dopamine
signaling. Evidence from animal models suggested that the
upregulation of D2 receptors in the frontal, parietal, temporal
and occipital lobes and the downregulation of D1 receptors in the
prefrontal and temporal cortices may be an important component
of the therapeutic response to neuroleptic drugs [48]. These
receptor blockades have been shown to promote oligodendrocyte
repopulation and remyelination of experimentally demyelinated
cells in mice [49]. We also found that nonresponders had more
severe WM damage as lower FA and higher MD in these fiber
bundles at baseline than responders. These findings together
suggest that the alterations in the IFOF, ILF and SLF may represent
neural markers of the severity and persistence of the signs and
symptoms of schizophrenia, and may compromise the potential
effects of antipsychotics.
This study has some limitations. First, participants in our study
were randomized to receive a single standardized treatment with
one of three antipsychotics, but the sample was too small to rule out
changes in response patterns based on different medications. Future
investigations are encouraged to provide a comparison of the
effectiveness of different drugs. Second, the study did not include a
placebo control group, so a potential placebo or time effect cannot
be excluded. Due to ethical issues, these effects are normally nested
in clinical studies and cannot be completely removed. Third, as a
machine learning study focusing on individual prediction, the sample
size in this study was relatively small, and it lack an independent
validation sample. Therefore, these findings should be externally
verified with larger samples in the future.
In conclusion, altered FA and MD of fiber tracts in the ATR,
corticospinal tract, callosum forceps minor, IFOF, ILF, and SLF were
related to the severity of symptoms in first-episode schizophrenia.
These effects on WM tracts are not influenced by pharmacother-
apy and therefore appear to be disease-related. These baseline
psychotic symptom-related WM tracts, especially ILF, IFOF, and
SLF, may serve as meaningful individualized predictors of
response to SGA. These results may represent an important first
step of the translational value of baseline brain structural
measures in precision psychiatry.
DATA AVAILABILITY
The data that support the findings of this study are available from the corresponding
author upon reasonable request.
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ACKNOWLEDGEMENTS
This study was supported by the National Major Project of Scientific and Technical
Supporting Programs of China during the 11th Five-year Plan Period (Grant No.
2017BAI17B04), the Key research and development project of Science and
Technology, department of Sichuan Province (22ZDFY2064, 2022YFS0179).
AUTHOR CONTRIBUTIONS
YC, HC, HD, and XY conceptualized the study. HD, SL, BZ, GZ, ZZ, SL, and HL collected
the data. YC and HC performed statistical analysis. YC drafted the manuscript, HC
critically reviewed the manuscript. All authors reviewed the manuscript and
approved the final version for submission.
COMPETING INTERESTS
The authors declare no competing interests.
ADDITIONAL INFORMATION
Supplementary information The online version contains supplementary material
available at https://doi.org/10.1038/s41398-023-02714-w.
Correspondence and requests for materials should be addressed to Hong Deng.
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