Prediction of vulnerability to bipolar disorder using multivariate neurocognitive patterns: a pilot study
Wu et al. Int J Bipolar Disord
Prediction of vulnerability to bipolar disorder using multivariate neurocognitive patterns: a pilot study
MonJ‑u Wu 0 1 3
Benson Mwangi 3
Ives Cavalcante Passos 3
Isabelle E. Bauer 3
Cao Bo 3
Thomas W. Frazier 2
Giovana B. Zunta‑Soares 3
Jair C. Soares 3
0 Department of Psychiatry & Behavioral Sciences, The University of Texas Health Science Center , 1941 East Road, Houston, TX 77054 , USA
1 Department of Psychiatry & Behav‐ ioral Sciences, The University of Texas Health Science Center , 1941 East Road, Houston, TX 77054 , USA
2 Cleveland Clinic Children's Hospital Center for Pediatric Behavioral Health , Cleveland, OH , USA
3 UT Center of Excellence on Mood Disorder, Department of Psychiatry and Behavioral Sciences, The University of Texas Science Center at Houston , Houston, TX , USA
Bipolar disorder (BD) is a common disorder with high reoccurrence rate in general population. It is critical to have objective biomarkers to identify BD patients at an individual level. Neurocognitive signatures including affective Go/ No‑ go task and Cambridge Gambling task showed the potential to distinguish BD patients from health controls as well as identify individual siblings of BD patients. Moreover, these neurocognitive signatures showed the ability to be replicated at two independent cohorts which indicates the possibility for generalization. Future studies will examine the possibility of combining neurocognitive data with other biological data to develop more accurate signatures.
Bipolar disorder; Neurocognition; Vulnerability; CANTAB; Machine learning
Bipolar disorder (BD) has a lifetime prevalence of 4–5%
in the general population. It is frequently associated with
high rates of morbidity, mortality, and completed suicides
(Mathers et al. 2006; Merikangas 2007; Nordentoft et al.
. It has been reported that only 20% of BD patients
experiencing a depressive episode are diagnosed with BD
within the first year of seeking treatment. This greatly
underscores the need for objective diagnostic and
vulnerability markers of this debilitating illness
et al. 2001)
. Noticeably, previous epidemiological studies
have reported that first-degree relatives of BD patients
have an increased tenfold risk of BD as compared to the
general population—which strongly highlights the role of
genetic factors to the etiology of BD
(Kessler et al. 1994;
Olvet et al. 2013)
. However, despite these facts, there
are no clinically useful biomarkers of vulnerability to
BD that guides the institution of prophylactic
interventions. These timely interventions may delay the onset
of BD and translate into better clinical outcomes such
as decreased rates of recurrence, less severe episodes
(Post et al. 2010), and reduced medical related costs due
to less hospitalizations.
Multiple studies have reported neurocognitive
abnormalities in BD patients as compared to demographically
matched healthy controls (HCs). These abnormalities
have primarily been shown in key cognitive domains
such as: executive function, sustained attention, verbal
learning, and working memory
(Robinson and Ferrier
2006; Torres et al. 2007; Arts et al. 2008; Bora et al. 2009;
Torres et al. 2010; Mann-Wrobel et al. 2011; Bourne et al.
2013; Bauer et al. 2015; Wu et al. 2016)
studies examining neurocognitive measurements in
firstdegree relatives of BD patients have also reported deficits
in unaffected first-degree relatives in similar
neurocognitive domains. A recent meta-analysis summarized
studies investigating neurocognitive endophenotypes in BD
and reported abnormalities in first-degree relatives of
BD patients in key domains such as: set-shifting,
processing speed, verbal learning, and response inhibition
(Bora et al. 2009)
. Similarly, in a recent review, Olvet
et al. reported a consistent theme on memory-related
deficits in unaffected twins and siblings of patients with
BD as compared to HCs
(Olvet et al. 2013)
verbal, declarative, and working memory deficits were
shown in unaffected siblings
(Gourovitch et al. 1999; Kéri
et al. 2001; Kieseppä et al. 2005; Christensen et al. 2006)
Moreover, several other studies have highlighted
executive function and verbal memory abnormalities as
candidate endophenotypes of BD following reported deficits
in these domains in first-degree relatives of BD patients
(Arts et al. 2008; Bora et al. 2009; Doyle et al. 2009)
However, while these studies have undeniably advanced our
understanding of vulnerability markers of BD, it remains
unknown whether reported abnormalities can objectively
identify unaffected individuals vulnerable to BD and at
an individual level. Noticeably, being able to predict an
individual participant’s probability of vulnerability to
BD based on a hazard-free and easily accessible
neurocognitive task could help in institution of individualized
prophylactic interventions and translate into favorable
To achieve this objective, we recruited 21 euthymic BD
patients (7 males, 14 females; age: 36.12 ± 16.55 years)
and 21 demographically matched HCs (5 males, 16
females; age: 36.08 ± 12.66 years) at the University of
North Carolina at Chapel Hill—a sample we refer to as
the discovery cohort. A set of neurocognitive task scores
were assessed for each individual using the Cambridge
neuropsychological test automated battery
(CANTAB). The nine assessed CANTAB neurocognitive tasks
include: Affective Go/No-Go, Big/Little Circle,
Cambridge Gambling Task, Choice Reaction Time, Motor
Screening, Match to Sample Visual Search, Rapid Visual
Processing, Spatial Recognition Memory, and Spatial
Span task. The essence and measurements of all nine
tasks are summarized in Table 1. As a second step, a
replication cohort of 15 BD patients (5 males, 10 females;
age: 32.67 ± 9.26 years) and 16 demographically matched
HCs (5 males, 11 females; age: 33.75 ± 10.95 years)
were assessed at the University of Texas Health
Science Center at Houston. A set of CANTAB
neurocognitive task measurements similar to the discovery
cohort was also assessed. Notably, in the second center
(replication cohort), an additional group of 15 age- and
gender-matched siblings (SI) (4 males, 11 females; age:
32.20 ± 11.69 years) of BD patients (non-affected with
BD) were also recruited and their CANTAB
measurements were assessed. These data were first used to ‘train’
a least absolute shrinkage selection operator (LASSO)
machine-learning algorithm in distinguishing patients
from HCs. Second, the established predictive signature
was further validated using an independent replication
cohort of BD patients and HCs (Fig. 1). Lastly, the extent
to which the validated predictive neurocognitive
signature may differentiate the siblings (SIs) from HCs and BD
patients was also examined.
The LASSO algorithm identified individual BD patients
from HCs in the discovery cohort with 69% accuracy, 76%
sensitivity, 62% specificity, 67% of positive predictive
values (PPV), 72% of negative predictive values (NPV), and
an area under receiver operating characteristic curve
(AUROC) of 0.6905 with Chi-square p = 0.0126 (Fig. 2
and Additional file 1: Table S1). In the discovery cohort,
predictor variables identified by the LASSO algorithm
as most relevant in distinguishing BD patients from HCs
(non-zero coefficients) include: number of omission errors
to negative stimuli on the Affective Go/No-Go task, delay
aversion, and the risk adjustment on the Cambridge
Gambling Task and the total number of hits on the Rapid Visual
Processing (Fig. 3 and Additional file 1: Table S2). In the
replication cohort, the LASSO model derived at the
discovery stage identified individual BD patients from HCs in
the replication cohort with 74% accuracy, 73% sensitivity,
75% specificity, 73% of PPV, 75% of NPV, and an AUROC
of 0.7417 (Fig. 4 and Additional file 1: Table S3). These
predictions were significant (Chi-square p = 0.007). Predicted
probability scores of HCs differed significantly from SIs
and BD patients with p = 0.027 and p = 0.008, respectively.
On the other hand, SIs were largely indistinguishable from
BD patients with p = 0.678. These tests were performed
using a non-parametric Kruskal–Wallis statistical test.
From a cognitive viewpoint, compared to HCs,
individuals with BD committed a greater number of errors
when exposed to negative stimuli. This finding provides
further support for the presence of a negative affective
bias which is reflected by impaired cognitive processing
resulting from exposure to negative stimuli in both adults
with BD and offspring of BD patients
Passarotti 2008; Abe et al. 2011; Passarotti et al. 2011, 2012;
Bauer et al. 2015)
. Furthermore, HCs had a higher quality
of risk adjustment on the CGT task compared with
individuals with BD
(Quraishi and Frangou 2002)
, which is
a reliable estimate of impulsivity and risk taking
et al. 2003)
. Therefore, our findings are consistent with
previous evidence that patients with BD have a high
reward-seeking response and are unable to delay
(Najt et al. 2007; Swann et al. 2009)
in spite of the absence of a diagnosis of BD, the at-risk
individuals displayed the tendency to make poorer
decisions compared with HCs. This finding is particularly
relevant because, to date, few studies have focused on the
cognitive functioning of siblings of BD patients.
Previous studies of unaffected siblings found that they scored
lower on tests of verbal learning, attention, and planning
than healthy individuals (Kéri et al. 2001;
Trivedi et al.
Kulkarni et al. 2010
Nehra et al. 2014
). Further, in
line with our findings, the magnitude of these cognitive
deficits of SIs has consistently been reported to be
intermediate between that of HCs and BD patients. Another
potential implication of our findings is that impulsivity,
a trait typically associated with BD
(Newman and Meyer
and underlying decision making and reward tasks
(Christodoulou et al. 2006)
is a potential marker of
vulnerability to BD in SIs.
The current study has some potential limitations. The
overall sample size in both discovery and replication
cohorts were small and therefore our results should be
regarded as preliminary. The discovery cohort was
relatively small as we only considered euthymic patients
at the signature discovery stage to avoid potential
confounders related to mood phase (e.g., depression, mania).
Six SI participants were diagnosed with other mood
disorders other than BD (e.g., major depression) and future
studies should examine this research question using an
SI cohort without any psychiatric diagnoses. BD patients
included in the discovery cohort were taking
psychotropic medications which may be a potential confounder
but also a reflection of standard clinical practice.
In conclusion, we report a study showing
neurocognitive signature able to distinguish individual BD patients
from HCs. We suggest this signature could be combined
with other biological features to potentially develop a
BD prediction model. However, the current study serves
as a proof-of-concept. Future studies will examine this
hypothesis using other biological markers (e.g.,
neuroimaging) as well as attempt to integrate multi-scale
biomarkers (e.g., neuroimaging and neurocognition) which
may potentially improve the current prediction results.
Additional file 1. Detailed prediction results in both discovery and
BD: bipolar disorders; HC: healthy control; SI: sibling; CANTAB: Cambridge
Neurocognitive Test Automated Battery; UNC: University of North Carolina at
Chapel Hill; UTHealth: University of Texas Health Science Center at Houston;
HAMD: Hamilton Depression Rating Scale; MADRS: Montgomery–Åsberg
Depression Rating Scale; YMRS: Young Mania Rating Scale; LASSO: Least
Absolute Selection Shrinkage Algorithm; PPV: positive predictive values; NPV:
negative predictive values; AUROC: area under receiver operating character‑
MW was in charge of data preprocessing, implementation of machine learn‑
ing algorithms, data interpretation, and manuscript preparation. BM partici‑
pated in the implementation of machine learning algorithms, data interpreta‑
tion, and manuscript preparation. IP participated in data preprocessing, data
interpretation, and manuscript preparation. IB participated in data preprocess‑
ing, data interpretation, and manuscript preparation. CB participated in data
interpretation and manuscript preparation. TF participated in data acquisition,
data interpretation, and manuscript preparation. GZ participated in data
acquisition, data interpretation, and manuscript preparation. JS participated in
data acquisition, data interpretation, and manuscript preparation. All authors
read and approved the final manuscript.
Prof. Soares has participated in research funded by Forest, Merck, BMS, GSK
and has been a speaker for Pfizer and Abbott. Dr. Frazier has received federal
funding or research support from, acted as a consultant to, received travel
support from, and/or received a speaker’s honorarium from the Simons
Foundation, the Ingalls Foundation, Forest Laboratories, Ecoeos, IntegraGen,
Kugona LLC, Shire Development, Bristol‑Myers Squibb, the National Institutes
of Health, and the Brain and Behavior Research Foundation. All other authors
have no interests to declare.
Availability of data and materials
All data were stored at secured places at the University of Texas Health Science
Center at Houston and can only be accessed by authorized personnel under
the supervision of IRB.
Consent for publication
All author gave their consent to publish this manuscript.
Ethics approval and consent to participate
This study was approved by the University of North Carolina at Chapel
Hill (UNC) and the University of Texas Health Science Center at Houston
(UTHealth) Institutional Review Boards. All participants signed informed con‑
sent before any study‑related procedures were performed.
This research was funded by NIH R01 MH 085667, the Dunn Foundation and
Pat Rutherford, Jr. Chair in Psychiatry at UTHealth to Jair C. Soares.
Springer Nature remains neutral with regard to jurisdictional claims in pub‑
lished maps and institutional affiliations.
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