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PERSPECTIVE

Personality network neuroscience: Promises and
challenges on the way toward a unifying
framework of individual variability

Kirsten Hilger1,2

and Sebastian Markett3

1Department of Psychology I, Julius-Maximilians University Würzburg, Würzburg, Germany
2Department of Psychology, Goethe University Frankfurt, Frankfurt am Main, Germany
3Department of Psychology, Humboldt-Universität zu Berlin, Germany

a n o p e n a c c e s s

j o u r n a l

Keywords: Personality network neuroscience (PNN), fMRI, Personality psychology, Trait theory,
Functional brain connectivity

ABSTRACT

We propose that the application of network theory to established psychological personality
conceptions has great potential to advance a biologically plausible model of human personality.
Stable behavioral tendencies are conceived as personality “traits.” Such traits demonstrate
considerable variability between individuals, and extreme expressions represent risk factors for
psychological disorders. Although the psychometric assessment of personality has more than
hundred years tradition, it is not yet clear whether traits indeed represent “biophysical entities”
with specific and dissociable neural substrates. For instance, it is an open question whether
there exists a correspondence between the multilayer structure of psychometrically derived
personality factors and the organizational properties of traitlike brain systems. After a short
introduction into fundamental personality conceptions, this article will point out how network
neuroscience can enhance our understanding about human personality. We will examine the
importance of intrinsic (task-independent) brain connectivity networks and show means to link
brain features to stable behavioral tendencies. Questions and challenges arising from each
discipline itself and their combination are discussed and potential solutions are developed.
We close by outlining future trends and by discussing how further developments of network
neuroscience can be applied to personality research.

INTRODUCTION

A major goal of psychology is to understand how and why people differ in thought and
behavior—and how such differences are organized in an individual’s personality. The foun-
dation of personality psychology is the observation that individual differences follow princi-
ples, that is, traits or dispositions, that are sufficiently stable within individuals, sufficiently
consistent between individuals, and sufficiently invariant to situational context to explain past
and to predict future behavior. Traits are hierarchically organized constructs that in their
entirety constitute a multidimensional space where each individual can be placed to describe
their personality and thus individuality (Allport, 1937; Eysenck, 1947; Guilford, 1959). The
main assumptions and implications of the trait concept as well as its presumed neurobiological
foundation will be introduced in the following section.

Citation: Hilger, K., & Markett, S. (2021).
Personality network neuroscience:
Promises and challenges on the way
toward a unifying framework of
individual variability. Network
Neuroscience, 5(3), 631–645. https://doi
.org/10.1162/netn_a_00198

DOI:
https://doi.org/10.1162/netn_a_00198

Received: 24 December 2020
Accepted: 22 April 2021

Competing Interests: The authors have
declared that no competing interests
exist.

Corresponding Authors:
Kirsten Hilger
kirsten.hilger@uni-wuerzburg.de
Sebastian Markett
sebastian.markett@hu-berlin.de

Handling Editor:
Emily Finn

Copyright: © 2021
Massachusetts Institute of Technology
Published under a Creative Commons
Attribution 4.0 International
(CC BY 4.0) license

The MIT Press

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Personality network neuroscience

Personality:
Stable emotional, motivational,
and cognitive dispositions that
characterize a person’s individuality
and explain his/her behavior across
situations and time.

Main Conceptions of Human Personality

The most renowned example for a taxonomy of personality traits is the five factor model
(Goldberg, 1990) with the basic traits neuroticism, extraversion, openness to experience,
agreeableness, and conscientiousness. The Big Five are based on the “lexical hypothesis”
stating that fundamental personality traits have left their trace in language and can be uncov-
ered through a purely data-driven approach capitalizing on similarities between different ad-
jectives in the dictionary (Allport & Odbert, 1936; Cattell, 1945). In general, trait theory views
traits as orthogonal and dimensional constructs that allow for each individual to be positioned
in a multidimensional trait space (Figure 1A). Traits produce consistent behavior across (trait-
relevant) situations. A person scoring at the upper end of a given trait (e.g., extraversion) will

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Figure 1. Schematic illustration of linking network neuroscience to personality research. (A) Personality is a multidimensional factorial space
where traits are organized as bipolar dimensional constructs (e.g., introversion vs. extraversion). Each individual can be classified along the
trait continuum, and the relative position compared to others is highly stable across time and situations. (B) Traits produce consistent behavior
across (trait-relevant) situations and time. A person scoring at the trait’s upper end will respond consistently stronger (+++) to relevant stimuli
than a person at the lower end (—). (C) Each situation is associated with a certain brain state (functional connectivity, network state). Such
states bear a traitlike connectivity component that is consistent across (trait-relevant) situations and time. (D) These traitlike connectivity com-
ponents represent the neural trait system, and presumably consist of structural, functional, and dynamic network characteristics. Mapping
these components to personality traits (big dashed line) is the key objective of personality network neuroscience (PNN).

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Personality network neuroscience

Conceptual nervous system:
A part of the central nervous system
that realizes a circumscribed
psychological concept such as a
personality trait.

respond consistently stronger to relevant stimuli (e.g., social cues) than a person with lower
scores on the respective trait (Figure 1B). Since its beginning, trait theory has posited that traits
are more than statistical abstractions of behavior and have a foundation within the human
brain, that is, neural systems whose interacting elements constitute the neural equivalent of
traits—a conceptual nervous system (Allport, 1937; Gray, 1972; Hebb, 1955; Pickering,
1997), and different lines of research have approached personality traits from a neurobiolog-
ical perspective (Corr, 2004; Gray, 1972; see Box 1).

Box 1.

Milestones of Personality Psychology from a Neuroscience Perspective

(cid:129) Gordon Allport (1937):

(cid:1) Introduction of the “trait” concept
(cid:1) Traits as “biophysical entities” (brain systems)

(cid:129) Hans Eysenck (1947):

(cid:1) Neuroticism (emotional stability) and extraversion (stimulation seeking)

as the major personality traits (revealed by factor analysis)

(cid:1) Neurobiological correlates of traits
(cid:1) Eysenck Personality Questionnaire (EPQ)

▪ Still frequently used (e.g., in the UK Biobank)

(cid:129) Joy P. Guilford (1959):

(cid:1) Hierarchical organization of personality traits

▪ Primary traits determine subordinate traits that in turn determine behavior

(cid:1) Identification of five traits by factor analyses
(cid:1) Paving the way toward the Big Five personality taxonomy

(cid:129) Jeffrey Gray (1972):

(cid:1) Reconceptualization of Eysenck’s traits based on neuropharmacology and

brain lesion studies in animal models

(cid:1) Essential role of approach and avoidance behavior
(cid:1) Reward Sensitivity Theory:

▪ Two brain systems: The behavioral activation system (BAS) with striatal

and thalamic substrates and the behavioral inhibition system (BIS) within
septohippocampal involvement

(cid:129) Robert Cloninger (1986):
(cid:1) Hierarchy of traits:

▪ Temperament traits: Innate dispositions for approach behavior, avoidance
behavior, and social reward (novelty seeking, harm avoidance, reward
dependence, persistence)

▪ Character traits: Traits develop through interaction of temperament traits
and the individual learning environment (self-directedness, cooperativeness,
self-transcendence)

(cid:1) Temperament and Character Inventory

▪ Widely applied in neuroimaging studies, including network neuroscience

investigations

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Personality network neuroscience

(cid:129) Paul Costa & Robert McCrae (1992):

(cid:1) The Big Five:

▪ Neuroticism, extraversion, openness to experience, agreeableness, and

conscientiousness

(cid:1) NEO personality inventory (NEO-PI) and the shorter NEO five-factor inventory

(NEO-FFI)
▪ Applied in numerous neuroimaging investigations (e.g., in the Human

Connectome Project)

(cid:129) Jaak Panksepp (1998):

(cid:1) Affective Neuroscience Theory:

▪ Separable subcortical circuitry for primary affect and motivation (revealed by

invasive brain stimulation and lesioning)

▪ Neural circuitry operates as trait system and forms the basis of human personality

(cid:1) Affective Neuroscience Personality Scales (ANPS):

▪ Personality questionnaire based on neuroscientific data rather than statistical

aggregation of lexical data

(cid:129) Jeffrey Gray & Neil McNaughton (2000):
(cid:1) Revised Reward Sensitivity Theory:

▪ Anxiety and fear as separable neural systems, and hence different personality

traits

(cid:1) r-RST questionnaire distinguishes individual differences in the BAS, the BIS,

and the flight-fight-freezing system (FFFS)

(cid:129) Kibeom Lee & Michael Ashton (2004):

(cid:1) Extensive reanalysis of previous factor analytical data sets
(cid:1) Claim: Big Five system should be amended by a sixth factor “Honesty-Humility”
(cid:1) HEXACO questionnaire as alternative to the NEO-FFI (widely used in psychology)

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More specifically, trait theory presumes that different traits dissociate into independent neural
substrates that represent different traits at the brain level and whose between-person variations
map directly onto variations in the respective behavioral trait. Furthermore, trait theory posits
that these neural substrates are characterized by the same traitlike properties as behavioral traits
such as invariance across situations and time (Figure 1C) and that they directly influence ongoing
information processing and behavior. It is proposed that each situation is characterized by a
unique brain state consisting of (a) a neural reaction elicited by the physical aspects of the stim-
ulus and (b) an idiosyncratic component associated with the individual trait level (Figure 1D).
Finally, trait theory assumes that the idiosyncratic component that reflects the trait on the neural
level overlaps with the neural systems responsible for the processing of trait-congruent situations
(e.g., social and reward-related brain circuitry in the case of the trait extraversion). This overlap
becomes apparent in neural signatures of ongoing information processing and can, thus, be
investigated via neuroscientific methods during trait-congruent situations. However, trait theory
also assumes that traits are more than the sum of associated behavioral and neural states. Parts of
the neural trait systems will therefore persist detached from ongoing information processing
states and should be accessible also by different approaches than task-constrained neuroim-
aging (e.g., via resting-state fMRI).

In the following we argue that the mapping of structural, functional, and dynamic network
characteristics of personality traits—a framework that we term personality network

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Personality network neuroscience

Graph theory:
Mathematical approach to model
complex network systems as a set of
nodes (e.g., brain regions) connected
by edges (e.g., neural connections).

Psychometrics:
A psychological discipline focused
on theories and techniques to
measure psychological constructs.

Internal consistency:
A technical term from psychometrics
that reflects how reliable an
instrument measures a psychological
construct.

Questionnaire items:
An item is the fundamental unit
of psychological assessment. A
questionnaire item usually describes
trait-congruent behavior and asks
how this description applies to the
test taker.

neuroscience (PNN)—holds exciting prospects to test key hypotheses from trait theory and to
ultimately identify such neural trait systems. Before we summarize the current state of the art and
outline how the connectome paradigm (Hagmann, 2005; Sporns et al., 2005) and network neu-
roscience with its rich methodology such as graph theory (Bassett & Sporns, 2017) can contrib-
ute to personality science more in detail, we will introduce the most common existing
approaches toward measuring personality traits.

Measuring Personality Differences

Personality traits are relatively broad dispositions that produce consistent behavior across a range of
different situations. While it would in principle be possible to infer an individual’s personality from
observed behavior, the current standard toward personality assessment is the use of psychometric
questionnaires. Such questionnaires consist of a list of standardized test items, which are selected
carefully to cover the trait’s most relevant aspects, to distinguish properly between individuals, and
to achieve highly similar results across different assessments of the same person. Usually, each
items consists of a short statement that is rated by the test takers as how well it describes themselves,
for example, “I am someone who worries a lot” (indicative of neuroticism). Different assessment
systems for personality traits have been proposed with varying length. Longer questionnaires allow
for more accurate and reliable assessments, but can be a burden for the participant. In general, it is
advised to select the minimum number of items so that the total questionnaire score allows for
confidential generalization to a person’s personality trait and to future behavior.

It is, of course, imperative to psychometrically validate personality questionnaires, that is, to
demonstrate the internal consistency of questionnaire items, to establish temporal stability (re-
liability), and to show theoretically meaningful associations with other data sources (external
validity). This can, for instance, be achieved through peer ratings (assuming that close friends
and relatives have similar abilities to rate a person’s personality) and behavioral experiments
(Markett et al., 2014; Perkins et al., 2007, 2009). Relying on (standardized) self-report person-
ality assessments is, however, not without criticism (Furnham, 1986). Alternative assessment
approaches to personality that harness the rich behavioral and personal data from social media
sites and smartphone-based assessments show promising results (Kosinski et al., 2013;
Marengo & Montag, 2020; Stachl et al., 2020), but until these attempts overcome the proof-
of-principle stage, self-report questionnaires remain the gold standard to assess a wide range of
behavioral dispositions in the most effective way.

PERSONALITY NETWORK NEUROSCIENCE

The goal of PNN is to identify and to integrate neural systems (or biophysical entities) associ-
ated with psychological trait conceptions within an integrated framework for human person-
ality. We propose that such an integrative framework may pave the way toward a more
mechanistic understanding of stable behavioral differences and we suggest that efforts toward
this goal will include (a) the identification of stable and traitlike individual differences in brain
network organization (existence of neural trait systems), (b) the mapping of these neural trait
systems onto known personality traits (associations between neural traits and personality trait
systems), (c) the demonstration of how these neural trait systems delineate in the sense of in-
dependent psychological traits and how the hierarchy and abstraction of personality traits is
reflected in the structure of the neural trait system (independence of neural trait systems and
trait-congruent organization), and (d) the investigation of how these neural trait systems lead to
differences in behavior and trait-congruent responses to external stimulation (neural traits pre-
dict trait-congruent behavior). These objectives were mostly addressed only in isolation by

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Personality network neuroscience

Intrinsic connectivity:
Correlated neural activity measured
during resting state, suggested to
reflect intrinsically generated
(as opposed to task-induced)
functional brain networks.

Functional dissociation:
A bidirectional one-to-one
relationship between a specific
neural substrate and a specific
psychological function.

Naturalistic viewing:
A more realistic, real-world-like
stimulation in a cognitive
neuroscience experiment (as
opposed to a narrow focus on simple
and controlled laboratory stimuli).

previous research. Regarding case (a), for example, individual deviations from group-level net-
works in the form of reliable and stable network variants have been observed across different
task states (Seitzman et al., 2019). Regarding case (b), several studies have demonstrated that
personality traits can explain individual differences in task-evoked and intrinsic (resting-state)
connectivity and activity (e.g., DeYoung, 2010; Dubois et al., 2018; Hsu et al., 2018; Markett
et al., 2016; Mulders et al., 2018; Tompson et al., 2018; Toschi et al., 2018). Regarding case
(c), studies mapped hierarchies and functional dissociations in affective and motivational brain
systems to different personality factors (Mobbs et al., 2007), and similar personality profiles
were linked to the similarity of brain connectivity patterns (Liu et al., 2019). Regarding case
(d), a particularly promising approach—intersubject representational similarity analysis—
attempts to dissociate stimulus-elicited neural activity with small between-subject variance from
idiosyncratic and trait-related neural activity patterns (for review, see Finn et al., 2020).
Specifically, this is done by residualizing neural time series from cross-subject correlations
and linking the remaining similarities in neural time series to similarities in personality traits
(Finn et al., 2020). This approach is based on intersubject correlation analysis (e.g., Gruskin
et al., 2020) but can, in contrast to the latter, also operate on the level of a single subject.
Intersubject representational similarity analysis has been successfully applied to fMRI during
tasks (Rhoads et al., 2020; van Baar et al., 2019) and during naturalistic viewing (Chen et al.,
2020; Nguyen et al., 2019) and complements other approaches that investigated relationships
between trait levels and brain responses during trait-congruent situations (Perkins et al., 2019).

Importantly, the identification of idiosyncrasies in brain structure and function and the confir-
mation of statistical relationships between organizational principles of the brain and behavioral
differences are not sufficient to describe a trait system in the form of a conceptual nervous system as
proposed by trait theory, that is, composed of dissociable neural systems that are stable over time
and influence behavior in trait-congruent situations. We state that this would require that different
lines of evidence are integrated into a common framework. It would, for instance, be necessary to
show strong correspondence between idiosyncratic network variants and psychometrically
measured personality traits. Psychometric traits are commonly based on questionnaire items that
provide relatively good insights into the traits’ nature. Since this is not yet the case for neuroimaging-
derived networks variants, establishing such correlations is a valuable step to aid interpretability.
Furthermore, it will be necessary to test whether actual behavior in trait-congruent situations can be
predicted from idiosyncratic aspects of brain organization and to determine neuroanatomical
borders for different trait systems. While multimodal association areas seem to carry the most
idiosyncratic information about individuals (e.g., Finn et al., 2015), trait systems are also likely to
include brain regions implicated in the processing of trait-relevant information, for example,
regions associated with social and reward-related information in the case of the trait extraversion.

Of note, although neural networks can also be derived from electrophysiological (e.g.,
Langer et al., 2012), structural (e.g., Privado et al., 2017), and many other neural data sources,
this perspective article will focus on functional networks modeled on the basis of functional
magnetic resonance imaging (fMRI). Inherent limitations and methodological challenges that
come up with introducing fMRI-based network neuroscience to personality research will be
discussed briefly in the next section.

CHALLENGES TO PERSONALITY NETWORK NEUROSCIENCE

Challenges Arising from Personality Conceptions and Its Measurements

Questionnaire data can be collected alongside neurological data to map individual variation
in personality traits to individual variation in, for example, brain network organization. While

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Personality network neuroscience

such approaches are widely used to study the “neural correlates” of personality, their strictly
correlative nature remains also their greatest limitation, particularly since naturally occurring
individual differences cannot be controlled experimentally. Situational context, in contrast,
can be systematically manipulated and can therefore inform whether implicated brain sys-
tems produce trait-relevant behavior and react differently to external stimulation in a trait-
congruent way (Markett et al., 2018). Furthermore, it is important to be aware of the fact that
the size of any such correlative association is inherently limited by the plausibly not perfect
reliability of both the personality assessment and the neuronal measure of interest (Sui et al.,
2020; Vul et al., 2009). Finally, any personality questionnaire relies on specific theoretical
assumptions about how personality is structured within the human mind. Thus, any person-
ality assessment can only be valid as long as these theories’ assumptions are met and the
theory may reflect “reality” (construct validity).

Challenges Arising from Network Neuroscience

Just like personality measures, measures of brain network interactions are also characterized
by imperfect reliability. This applies to both the functional connectivity itself as well as to the
measures calculated on the basis of the respective brain connections. Although single brain
connections seem to be characterized by poor to medium reliability, the global pattern on
whole-brain connectivity seems to be quite reliable (Noble et al., 2019; Termenon et al.,
2016). Furthermore, specific parameters were identified that critically impact the reliability
of functional connectivity estimates (e.g., localization of brain connections, Mueller et al.,
2015; scan duration, Birn et al., 2013; Demetriou et al., 2018; Hallquist & Hillary, 2018),
which demonstrates the necessity of parameter-dependent reliability estimations and suggests
opportunities to improve on reliability.

Another attempt to increase reliability in functional connectivity research is to subsume
the most fundamental brain network characteristics within specific mathematical measures,
such as graph metrics, that can provide insights into preferred routes of neural communi-
cation and into the functional specialization of brain networks (Rubinov & Sporns, 2010).
However, the reliability of graph measures is also far away from perfect. While Telesford
et al. (2013) reported higher reliability for clustering, path length, global and local efficiency
than for degree, Braun et al. (2012) differentiated between first-order metrics (computed
directly on functional connections) and second-order metrics (computed on first-order met-
rics), and suggested higher reliability for the latter. Beyond graph metrics, additional mea-
sures aiming to capture fundamental aspects of functional brain connectivity have recently
been proposed and represent promising candidates for gaining deeper insights into the
neural reflection of personality differences. For instance, Seitzman et al. (2019) suggest
network variants as capturing important traitlike aspects of functional connectivity, while
Salehi et al. (2020) propose specific relevance of temporally fluctuating individual network
parcellations. The advent of time-resolved (dynamic) connectivity offers opportunities over
and above the investigation of static (time-averaged) networks. For example, graph metrics
can also be derived from edge functional connectivity (Faskowitz et al., 2019), bipartitions
may complement our understanding about dynamic communication between large-scale
brain networks (Sporns et al., 2020), and high-amplitude events representing states of high
brain-wide cofluctuation can be evaluated for their potential to capture meaningful
between-person differences in information processing dispositions (Esfahlani et al., 2020).
Finally, the method of connectome embedding allows one to map individual differences
through brain structure to brain function and to ultimately link them to behavioral variations
(Levakov et al., 2021).

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Personality network neuroscience

Another significant challenge to network analyses arises from variations in fMRI preprocess-
ing pipelines (e.g., global signal regression, motion correction; Parkes et al., 2018) and uncer-
tainty about the definition of network nodes and edges (van Wijk et al., 2010). Although most
often nodes are defined on the basis of anatomical or functional brain atlases, concurring ap-
proaches are also available (e.g., Gordon et al., 2016), and there is still no consensus about the
optimal way of parcellating the brain into meaningful partitions. However, different parcella-
tions result in networks of different size and density, which can critically affect the reliability
and robustness of network measures and thus impede comparability across different persons,
groups, and studies (Zalesky et al., 2010). Attempts to deal with this challenge imply comput-
ing measures on different parcellation schemes and the development of more standardized
parcellation approaches (e.g., Schaefer et al., 2018). Similarly, there is uncertainty about the
definition of network edges, for example, weighted versus unweighted, density thresholds for
binary networks, parameters for sliding-window approaches in dynamic network analyses—
most of them having a critical impact on the comparability of computed measures (Ginetstet
et al., 2011; van Wijk et al., 2010). However, these challenges can also, in principle, be amelio-
rated through demonstrating the robustness of results across different edge definitions or by
temporally unresolving the correlation metric (Faskowitz et al., 2019). Finally, the selection of
graph metrics also varies largely between different studies, and the alignment from graph
metrics onto neurobiological processes is far away from being clear. Recommendations about a
minimum set of metrics that should be reported in every study (Hallquist & Hillary, 2018) as well as
further research on the biological underpinnings of graph metrics (e.g., via task-state connectivity;
Cole et al., 2021; Greene et al., 2020) may help to address these limitations.

To conclude, most research suggests that functional connectivity may—despite its inherent
limitations—represent a valuable tool to study neural correlates of stable individual character-
istics such as human personality (Dubois et al., 2018; Gratton et al., 2018; Mueller et al.,
2015; Noble et al., 2019; Sui et al., 2020). The existence of new candidate measures and
the advancement of innovative ways of dynamic network analyses complement the overall
optimistic picture and highlight the need for further developing the transfer of these methods
to personality science.

Challenges from Combining Personality Research and Network Neuroscience

Some of the limitations and challenges outlined above become especially critical when inves-
tigating neural correlates of individual differences (in personality) as they can, when ignored or
not properly addressed, induce spurious associations between neural and psychological mea-
sures. Already during preprocessing, for instance, motion correction can induce bias in indi-
vidual difference analyses. If, for example, the numbers of excluded fMRI frames (scrubbing) or
the numbers of model regressors (spike regression) (Parkes et al., 2018)—both potentially af-
fecting connectivity measures in an unwarranted way ( Yan et al., 2013)—vary systematically
with the (personality) variable of interest, this would induce artificially associations between
neural and psychological measures. Therefore, motion correction strategies retaining the same
amount of data points across participants (e.g., linear regression of 24 motion parameters;
Satterthwaite et al., 2013) and reducing the loss of degrees of freedom (e.g., ICA-AROMA;
Pruim et al., 2015) are recommended for a network neuroscience of personality.

Another critical issue is the definition of a common node parcellation scheme. Although
most connectivity measures require the same number of nodes across subjects to allow for com-
parisons, it is obvious that individuals vary in both their anatomical subdivisions and in the way
their brains are functionally organized into different subnetworks or modules (e.g., Hilger et al.,

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Graph-theoretical measures:
Specific measures that allow one to
characterize networks more in detail,
thus providing insights into, for
example, preferred routes of
communication flow.

2017). Although some software packages provide options to partially account for individual
differences in morphological characteristics (e.g., Freesurfer), the applied common parcellation
scheme may still be more similar to the “true” individual partition in some subjects than in
others. When the variance of the amount of this difference (individual partition vs. common
partition) is not completely orthogonal to the variation in the individual difference measure, this
can potentially confound the association between computed connectivity measures and the
(personality) variable of interest. Recent advances toward individual node parcellations may
help to develop new ways of comparing connectivity measures by simultaneously accounting
for these differences (Salehi et al., 2020).

Finally, graph-theoretical measures can be crucially influenced by individual differences in
mean connectivity strength (van Wijk et al., 2010). Although such individual differences can
be meaningful, they complicate the interpretation of network measures. This seems to be
especially problematic for efficiency measures (and other shortest paths metrics; Ginestet
et al., 2011), while other measures seem to be more robust against such variations. In binary
networks this issue can partially be solved by the choice of a proportional threshold retaining
the same number of network edges for all subjects (but note the danger of including more
spurious connections in networks with low mean connectivity strength; van den Heuvel
et al., 2017), while weighted network measures are inherently affected by variations in net-
work density and do, therefore, always represent a mixture of topological differences in net-
work structure and differences in network density. Both can give rise to meaningful
associations with personality variations, but for different reasons. Thus, especially for PNN,
it is recommended to compute graph measures on unweighted (proportionally thresholded)
and weighted networks and to explore in both cases the effect of controlling for individual
differences in mean connectivity strength (for a comprehensive discussion and recommenda-
tions, see Hallquist & Hillary, 2018).

FUTURE TRENDS AND NEW DIRECTIONS

Major Questions of Personality Psychology Addressed with New Methods

As outlined in detail above, PNN aims to identify and to integrate trait-related neural systems
within an integrated framework for human personality. New connectivity-based methods
such as connectome fingerprinting (Finn et al., 2015; Kumar et al., 2018), network variants
analysis (Seitzman et al., 2019), and intersubject representation similarity analysis (Finn
et al., 2020) provide a toolbox to relate idiosyncrasies in brain function to psychometric
personality traits. Future work may extend these approaches by including the concurrent
measurement of multiple personality traits, by studying a wider range of trait-relevant situa-
tions in the form of experimental tasks or naturalistic viewing paradigms (e.g., of trait-relevant
movie scenes), and by predicting behavioral variability in new trait-relevant situations from
neural data. Another important aspect is the demonstration of the persistence of neural systems
(or biophysical entities) over time, inside and outside of trait-relevant situations: This may
require additional approaches to investigate the relationship between task activations and
structural connectivity (Medaglia et al., 2017) as well as between functional connectivity
assessed during task and rest (Cole et al., 2021). Once neural systems (or biophysical entities)
for different traits have been delineated, it will also become relevant to study their interaction.
Although trait theory suggests different traits as dissociable independent systems, multiple traits
are supposed to influence behavior in a given situation through complex interactions.
Understanding these interactions will therefore be relevant to predict individual behavior in
new situations.

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Open Questions and Further Developments

The network science of personality is currently restrained by pressing paradigmatic and meth-
odological issues. Many imaging studies are still low in statistical power, particularly when it
comes to brain-behavior correlations (Cremers et al., 2017). Possible solutions are the collab-
orative sharing of data and meta-analyses of published findings. Since the inception of the
1000 Functional Connectomes Project (Biswal et al., 2010), data were released from several
thousand participants. Through other large-scale endeavors such as the Human Connectome
Project (HCP), the Chimgen Project, and the UK Biobank, the number of included participants
lies currently in the upper five digits. Such sample sizes are actually needed, as correlations
between neuroimaging measures and psychological variables stabilize only in sufficiently
large samples (Marek et al., 2020; Sui et al., 2020). The problem for personality research is
that most available datasets do not contain a broad phenotypically (personality) assessment
(except for the HCP that includes the NEO-FFI for its 1,200 participants), or if they do, make
use of different tools (but see, e.g., Nooner et al., 2012). As long as they measure similar con-
structs, different assessment tools can be harmonized and combined—a strategy applied in mo-
lecular genetics where even larger sample sizes are required (Baselmans et al., 2019; Montag
et al., 2020). Such strategies may also be viable for neuroimaging, particularly since it has been
shown that brain-personality relationships generalize across datasets and psychometric approaches
(Jiang et al., 2018). Also, further developments from the predominantly applied explanatory
correlative approaches toward more predictive machine learning-based analyses (implying
cross-validation) can further contribute to increase the reliability and reproducibility of find-
ings when adopted on large samples (for an exemplary study in the PNN domain, see, e.g.,
Dubois et al., 2018). This, however, does only apply to situations where any personality data
has been collected at all. Therefore, we would like to encourage researchers to include at least
a basic personality assessment (e.g., the Big Five Inventory; John & Srivastava, 1999) into their
projects, particularly when they plan to make their data publicly available.

Meta-analyses integrating results across published studies are a second route our field might
consider. Cognitive neuroscience, for instance, has benefited tremendously from its meta-
analytical approaches (Laird et al., 2005; Yarkoni et al., 2011). Classic cognitive neuroscience
studies rely on a unified statistical framework (mass-univariate application of general linear
models) within a universal topological space (Montreal Neurological Institute). Both are ideal
preconditions for coordinate-based meta-analyses. Network neuroscience studies, on the other
hand, show a wide variety in their basic approaches such as different brain parcellations, con-
nectivity metrics, and resolution levels. A common reference frame for PNN investigations
would be highly desirable.

Finally, the establishment of a link between the hierarchical modular structure of person-
ality factors and the multilayer architecture of the human brain does, in principle, require a
joint network approach, that is, the application of network measures on both neuroimaging
and personality assessment data. Recently, pioneering studies from clinical psychology also
demonstrated the usefulness of network analyses to questionnaire data (Duek et al. 2020;
Taylor et al., 2020; for review, see Burger et al., 2020; Hofmann et al., 2020) and do thus
suggest promising means—also for the further development of PNN.

Toward an Individualized Precision Neuroscience

Finally, progress toward identification of neural trait systems (i.e., biophysical entities) and the
mapping of these to psychological personality conceptions as outlined in this article would not
only benefit psychology but would also be of key interest to psychiatry and clinical

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neuroscience. Personality traits overlap etiologically with psychiatric categories (Okbay et al.,
2016), and manifestations at the extremes of individual variation play a key role in several
conditions (Krueger & Markon, 2006). Furthermore, personality characteristics within the nor-
mal range are indicative for the choice of treatment and provide information about potential
risk factors, suggesting their potential for the development of personalized medicine
(Ziegelstein, 2017). Including personality assessments into new data collection and advancing
a PNN could therefore also benefit the identification of clinically relevant biomarkers.

AUTHOR CONTRIBUTIONS

Kirsten Hilger: Conceptualization; Project administration; Writing – original draft; Writing –
review & editing. Sebastian Markett: Conceptualization; Project administration; Writing – original
draft; Writing – review & editing.

FUNDING INFORMATION

Kirsten Hilger, Deutsche Forschungsgemeinschaft (https://dx.doi.org/10.13039/501100001659),
Award ID: HI 2185/1.

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