PERSPECTIVA
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, Alemania
2Department of Psychology, Goethe University Frankfurt, Frankfurt am Main, Alemania
3Department of Psychology, Universidad Humboldt de Berlín, Alemania
un acceso abierto
diario
Palabras clave: Personality network neuroscience (PNN), resonancia magnética funcional, Personality psychology, Trait theory,
Functional brain connectivity
ABSTRACTO
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. Por ejemplo, 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.
INTRODUCCIÓN
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, eso es, 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). El
main assumptions and implications of the trait concept as well as its presumed neurobiological
foundation will be introduced in the following section.
Citación: Hilger, K., & Markett, S. (2021).
Personality network neuroscience:
Promises and challenges on the way
toward a unifying framework of
individual variability. Red
Neurociencia, 5(3), 631–645. https://doi
.org/10.1162/netn_a_00198
DOI:
https://doi.org/10.1162/netn_a_00198
Recibió: 24 December 2020
Aceptado: 22 Abril 2021
Conflicto de intereses: Los autores tienen
declaró que no hay intereses en competencia
existir.
Autores correspondientes:
Kirsten Hilger
kirsten.hilger@uni-wuerzburg.de
Sebastian Markett
sebastian.markett@hu-berlin.de
Editor de manejo:
Emily Finn
Derechos de autor: © 2021
Instituto de Tecnología de Massachusetts
Publicado bajo Creative Commons
Atribución 4.0 Internacional
(CC POR 4.0) licencia
La prensa del MIT
yo
D
oh
w
norte
oh
a
d
mi
d
F
r
oh
metro
h
t
t
pag
:
/
/
d
i
r
mi
C
t
.
metro
i
t
.
/
/
t
mi
d
tu
norte
mi
norte
a
r
t
i
C
mi
–
pag
d
yo
F
/
/
/
/
/
5
3
6
3
1
1
9
6
0
4
7
7
norte
mi
norte
_
a
_
0
0
1
9
8
pag
d
.
t
F
b
y
gramo
tu
mi
s
t
t
oh
norte
0
8
S
mi
pag
mi
metro
b
mi
r
2
0
2
3
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). En general, trait theory views
traits as orthogonal and dimensional constructs that allow for each individual to be positioned
in a multidimensional trait space (Figura 1A). Traits produce consistent behavior across (rasgo-
relevant) situations. A person scoring at the upper end of a given trait (p.ej., extraversion) will
yo
D
oh
w
norte
oh
a
d
mi
d
F
r
oh
metro
h
t
t
pag
:
/
/
d
i
r
mi
C
t
.
metro
i
t
.
t
/
/
mi
d
tu
norte
mi
norte
a
r
t
i
C
mi
–
pag
d
yo
F
/
/
/
/
/
5
3
6
3
1
1
9
6
0
4
7
7
norte
mi
norte
_
a
_
0
0
1
9
8
pag
d
t
.
F
b
y
gramo
tu
mi
s
t
t
oh
norte
0
8
S
mi
pag
mi
metro
b
mi
r
2
0
2
3
Cifra 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 (p.ej., 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 (conectividad funcional, network state). Semejante
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. Cartografía
these components to personality traits (big dashed line) is the key objective of personality network neuroscience (PNN).
Neurociencia en red
632
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 (p.ej., social cues) than a person with lower
scores on the respective trait (Figura 1B). Since its beginning, trait theory has posited that traits
are more than statistical abstractions of behavior and have a foundation within the human
cerebro, eso es, 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 (p.ej., 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) dentro
septohippocampal involvement
(cid:129) Robert Cloninger (1986):
(cid:1) Hierarchy of traits:
▪ Temperament traits: Innate dispositions for approach behavior, avoidance
comportamiento, and social reward (novelty seeking, harm avoidance, premio
dependencia, 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
Neurociencia en red
633
yo
D
oh
w
norte
oh
a
d
mi
d
F
r
oh
metro
h
t
t
pag
:
/
/
d
i
r
mi
C
t
.
metro
i
t
.
t
/
/
mi
d
tu
norte
mi
norte
a
r
t
i
C
mi
–
pag
d
yo
F
/
/
/
/
/
5
3
6
3
1
1
9
6
0
4
7
7
norte
mi
norte
_
a
_
0
0
1
9
8
pag
d
t
.
F
b
y
gramo
tu
mi
s
t
t
oh
norte
0
8
S
mi
pag
mi
metro
b
mi
r
2
0
2
3
Personality network neuroscience
(cid:129) Paul Costa & Robert McCrae (1992):
(cid:1) The Big Five:
▪ Neuroticism, extraversion, openness to experience, agreeableness, y
conscientiousness
(cid:1) NEO personality inventory (NEO-PI) and the shorter NEO five-factor inventory
(NEO-FFI)
▪ Applied in numerous neuroimaging investigations (p.ej., 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) Afirmar: 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)
yo
D
oh
w
norte
oh
a
d
mi
d
F
r
oh
metro
h
t
t
pag
:
/
/
d
i
r
mi
C
t
.
metro
i
t
.
t
/
/
mi
d
tu
norte
mi
norte
a
r
t
i
C
mi
–
pag
d
yo
F
/
/
/
/
/
5
3
6
3
1
1
9
6
0
4
7
7
norte
mi
norte
_
a
_
0
0
1
9
8
pag
d
t
.
F
b
y
gramo
tu
mi
s
t
t
oh
norte
0
8
S
mi
pag
mi
metro
b
mi
r
2
0
2
3
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. Además, trait theory posits
that these neural substrates are characterized by the same traitlike properties as behavioral traits
such as invariance across situations and time (Figura 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).
Finalmente, 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
(p.ej., 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, de este modo, ser
investigated via neuroscientific methods during trait-congruent situations. Sin embargo, 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 (p.ej., 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
Neurociencia en red
634
Personality network neuroscience
Graph theory:
Mathematical approach to model
complex network systems as a set of
nodos (p.ej., regiones del cerebro) conectado
by edges (p.ej., 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
construir.
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.
neurociencia (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 & despreciar, 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, y
to achieve highly similar results across different assessments of the same person. Usually, cada
items consists of a short statement that is rated by the test takers as how well it describes themselves,
Por ejemplo, “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. En general, es
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.
Es, por supuesto, imperative to psychometrically validate personality questionnaires, eso es, a
demonstrate the internal consistency of questionnaire items, to establish temporal stability (re-
liability), and to show theoretically meaningful associations with other data sources (externo
validity). This can, por ejemplo, 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, sin embargo, 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
sistemas), (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), y (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
Neurociencia en red
635
yo
D
oh
w
norte
oh
a
d
mi
d
F
r
oh
metro
h
t
t
pag
:
/
/
d
i
r
mi
C
t
.
metro
i
t
.
/
t
/
mi
d
tu
norte
mi
norte
a
r
t
i
C
mi
–
pag
d
yo
F
/
/
/
/
/
5
3
6
3
1
1
9
6
0
4
7
7
norte
mi
norte
_
a
_
0
0
1
9
8
pag
d
.
t
F
b
y
gramo
tu
mi
s
t
t
oh
norte
0
8
S
mi
pag
mi
metro
b
mi
r
2
0
2
3
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 (como
opposed to a narrow focus on simple
and controlled laboratory stimuli).
previous research. Regarding case (a), Por ejemplo, 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 (p.ej., 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).
Específicamente, 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 (p.ej., 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
tareas (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).
En tono rimbombante, 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, eso es, 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, por ejemplo, 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.
Además, 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 (p.ej., Finn et al., 2015), trait systems are also likely to
include brain regions implicated in the processing of trait-relevant information, Por ejemplo,
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 (p.ej.,
Langer et al., 2012), structural (p.ej., 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 (resonancia magnética funcional). 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, Por ejemplo, brain network organization. Mientras
Neurociencia en red
636
yo
D
oh
w
norte
oh
a
d
mi
d
F
r
oh
metro
h
t
t
pag
:
/
/
d
i
r
mi
C
t
.
metro
i
t
.
t
/
/
mi
d
tu
norte
mi
norte
a
r
t
i
C
mi
–
pag
d
yo
F
/
/
/
/
/
5
3
6
3
1
1
9
6
0
4
7
7
norte
mi
norte
_
a
_
0
0
1
9
8
pag
d
.
t
F
b
y
gramo
tu
mi
s
t
t
oh
norte
0
8
S
mi
pag
mi
metro
b
mi
r
2
0
2
3
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). Además, 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). Finalmente, any personality questionnaire relies on specific theoretical
assumptions about how personality is structured within the human mind. De este modo, 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). Además, specific parameters were identified that critically impact the reliability
of functional connectivity estimates (p.ej., 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 & despreciar, 2010).
Sin embargo, 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 y otros. (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. Por ejemplo, Seitzman et al. (2019) sugerir
network variants as capturing important traitlike aspects of functional connectivity, mientras
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) redes. Por ejemplo, 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
redes cerebrales (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).
Finalmente, 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).
Neurociencia en red
637
yo
D
oh
w
norte
oh
a
d
mi
d
F
r
oh
metro
h
t
t
pag
:
/
/
d
i
r
mi
C
t
.
metro
i
t
.
/
/
t
mi
d
tu
norte
mi
norte
a
r
t
i
C
mi
–
pag
d
yo
F
/
/
/
/
/
5
3
6
3
1
1
9
6
0
4
7
7
norte
mi
norte
_
a
_
0
0
1
9
8
pag
d
.
t
F
b
y
gramo
tu
mi
s
t
t
oh
norte
0
8
S
mi
pag
mi
metro
b
mi
r
2
0
2
3
Personality network neuroscience
Another significant challenge to network analyses arises from variations in fMRI preprocess-
ing pipelines (p.ej., 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 (p.ej., Gordon et al., 2016), and there is still no consensus about the
optimal way of parcellating the brain into meaningful partitions. Sin embargo, 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,
grupos, 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 (p.ej., Schaefer et al., 2018). Similarmente, there is uncertainty about the
definition of network edges, Por ejemplo, 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). Sin embargo, 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). Finalmente, 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) así como
further research on the biological underpinnings of graph metrics (p.ej., via task-state connectivity;
Cole y col., 2021; Greene et al., 2020) may help to address these limitations.
Para concluir, 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, por ejemplo, motion correction can induce bias in indi-
vidual difference analyses. Si, Por ejemplo, the numbers of excluded fMRI frames (scrubbing) o
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
con el (personality) variable of interest, this would induce artificially associations between
neural and psychological measures. Por lo tanto, motion correction strategies retaining the same
amount of data points across participants (p.ej., linear regression of 24 motion parameters;
Satterthwaite et al., 2013) and reducing the loss of degrees of freedom (p.ej., 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. A pesar de
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 (p.ej., Hilger et al.,
Neurociencia en red
638
yo
D
oh
w
norte
oh
a
d
mi
d
F
r
oh
metro
h
t
t
pag
:
/
/
d
i
r
mi
C
t
.
metro
i
t
.
/
/
t
mi
d
tu
norte
mi
norte
a
r
t
i
C
mi
–
pag
d
yo
F
/
/
/
/
/
5
3
6
3
1
1
9
6
0
4
7
7
norte
mi
norte
_
a
_
0
0
1
9
8
pag
d
t
.
F
b
y
gramo
tu
mi
s
t
t
oh
norte
0
8
S
mi
pag
mi
metro
b
mi
r
2
0
2
3
Personality network neuroscience
Graph-theoretical measures:
Specific measures that allow one to
characterize networks more in detail,
thus providing insights into, para
ejemplo, preferred routes of
communication flow.
2017). Although some software packages provide options to partially account for individual
differences in morphological characteristics (p.ej., Freesurfer), the applied common parcellation
scheme may still be more similar to the “true” individual partition in some subjects than in
otros. 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, este
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).
Finalmente, 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, por lo tanto, 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. De este modo, 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-
ciones, 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
análisis (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 (p.ej., 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) con el tiempo, 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 y col., 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.
Neurociencia en red
639
yo
D
oh
w
norte
oh
a
d
mi
d
F
r
oh
metro
h
t
t
pag
:
/
/
d
i
r
mi
C
t
.
metro
i
t
.
/
t
/
mi
d
tu
norte
mi
norte
a
r
t
i
C
mi
–
pag
d
yo
F
/
/
/
/
/
5
3
6
3
1
1
9
6
0
4
7
7
norte
mi
norte
_
a
_
0
0
1
9
8
pag
d
t
.
F
b
y
gramo
tu
mi
s
t
t
oh
norte
0
8
S
mi
pag
mi
metro
b
mi
r
2
0
2
3
Personality network neuroscience
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
Proyecto (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) evaluación
(except for the HCP that includes the NEO-FFI for its 1,200 Participantes), or if they do, make
use of different tools (but see, p.ej., 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). También, 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, ver, p.ej.,
Dubois et al., 2018). Este, sin embargo, does only apply to situations where any personality data
has been collected at all. Por lo tanto, we would like to encourage researchers to include at least
a basic personality assessment (p.ej., 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, por ejemplo, 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
modelos) within a universal topological space (Instituto Neurológico de Montreal). Both are ideal
preconditions for coordinate-based meta-analyses. Network neuroscience studies, en el otro
mano, show a wide variety in their basic approaches such as different brain parcellations, estafa-
nectivity metrics, and resolution levels. A common reference frame for PNN investigations
would be highly desirable.
Finalmente, 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, eso es, the application of network measures on both neuroimaging
and personality assessment data. Recientemente, 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
Finalmente, progress toward identification of neural trait systems (es decir., biophysical entities) y el
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
Neurociencia en red
640
yo
D
oh
w
norte
oh
a
d
mi
d
F
r
oh
metro
h
t
t
pag
:
/
/
d
i
r
mi
C
t
.
metro
i
t
.
/
t
/
mi
d
tu
norte
mi
norte
a
r
t
i
C
mi
–
pag
d
yo
F
/
/
/
/
/
5
3
6
3
1
1
9
6
0
4
7
7
norte
mi
norte
_
a
_
0
0
1
9
8
pag
d
t
.
F
b
y
gramo
tu
mi
s
t
t
oh
norte
0
8
S
mi
pag
mi
metro
b
mi
r
2
0
2
3
Personality network neuroscience
neurociencia. 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
condiciones (krüger & Markon, 2006). Además, 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.
CONTRIBUCIONES DE AUTOR
Kirsten Hilger: Conceptualización; Administración de proyecto; Escritura – borrador original; Writing –
revisar & edición. Sebastian Markett: Conceptualización; Administración de proyecto; Writing – original
borrador; Escritura – revisión & edición.
INFORMACIÓN DE FINANCIACIÓN
Kirsten Hilger, Deutsche Forschungsgemeinschaft (https://dx.doi.org/10.13039/501100001659),
Award ID: HI 2185/1.
REFERENCIAS
Allport, GRAMO. W.. (1937). Personality: A psychological interpretation.
Nueva York: h. Holt and Co.
Allport, GRAMO. w., & Odbert, h. S. (1936). Trait-names: A psycho-
lexical study. Psychological Monographs, 41(1). https://doi.org
/10.1037/h0093360
Baselmans, B. METRO. l., Jansen, r., Ip, h. F., Dongen, j. camioneta, Abdellaoui,
A., Weijer, METRO. PAG. van de, Bao, y., Smart, METRO., Kumari, METRO.,
Willemsen, GRAMO., Hottenga, J.-J., Boomsma, D. I., Geus, mi. j. C. de,
Nivard, METRO. GRAMO., & Bartels, METRO. (2019). Multivariate genome-wide
analyses of the well-being spectrum. Nature Genetics, 51(3), 445.
https://doi.org/10.1038/s41588-018-0320-8, PubMed: 30643256
bassett, D. S., & despreciar, oh. (2017). Network neuroscience. Naturaleza
Neurociencia, 20(3), 353–364. https://doi.org/10.1038/nn.4502,
PubMed: 28230844
Hijo, R. METRO., Molloy, mi. K., Patriat, r., parker, T., Meier, t. B., Kirk,
GRAMO. r., Nair, V. A., Meyerand, METRO. MI., & Prabhakaran, V. (2013).
The effect of scan length on the reliability of resting-state fMRI
connectivity estimates. NeuroImagen, 83, 550–558. https://doi
.org/10.1016/j.neuroimage.2013.05.099, PubMed: 23747458
Biswal, B. B., Mennes, METRO., Zuo, X.-N., Gohel, S., Kelly, C., Herrero,
S. METRO., beckman, C. F., Adelstein, j. S., Buckner, R. l.,
Colcombe, S., Dogonowski, A.-M., Ernst, METRO., Fair, D., Hampson,
METRO., Hoptman, METRO. J., Hyde, j. S., Kiviniemi, V. J., kötter, r., li,
S.-J., … Milham, METRO. PAG. (2010). Toward discovery science of
human brain function. Actas de la Academia Nacional de
Ciencias de los Estados Unidos de América, 107(10), 4734–4739.
https://doi.org/10.1073/pnas.0911855107, PubMed: 20176931
Braun, Ud., Plichta, METRO. METRO., Esslinger, C., Sauer, C., Haddad, l.,
Grimm, o., Mier, D., Mohnke, S., Heinz, A., Erk, S., walter,
h., Seiferth, NORTE., Kirsch, PAG., & Meyer-Lindenberg, A. (2012).
Test–retest reliability of resting-state connectivity network char-
acteristics using fMRI and graph theoretical measures.
NeuroImagen, 59(2), 1404–1412. https://doi.org/10.1016/j
.neuroimage.2011.08.044, PubMed: 21888983
Burger, J., Stroebe, METRO. S., Perrig-Chiello, PAG., Schut, h. A., Spahni, S.,
Eisma, METRO. C., & Frito, mi. I. (2020). Bereavement or breakup:
Differences in networks of depression. Journal of Affective
Disorders, 267, 1–8. https://doi.org/10.1016/j.jad.2020.01.157,
PubMed: 32063559
Cattell, R. B. (1945). The description of personality: Principles and
findings in a factor analysis. The American Journal of Psychology,
58(1), 69–90. https://doi.org/10.2307/1417576
Chen, PAG. h. A., Jolly, MI., Cheong, j. h., & Chang, l. j. (2020).
Intersubject representational similarity analysis reveals individual
variations in affective experience when watching erotic movies.
NeuroImagen, 216, 116851. https://doi.org/10.1016/j.neuroimage
.2020.116851, PubMed: 32294538
Cloninger, C. R. (1986). A unified biosocial theory of personality
and its role in the development of anxiety states. Psychiatric
Developments, 3(2), 167–226.
Col, METRO. w., Ito, T., Cocuzza, C., & Sanchez-Romero, R. (2021).
The functional relevance of task-state functional connectivity.
The Journal of Neuroscience, JN-RM-1713-20. https://doi.org
/10.1523/JNEUROSCI.1713-20.2021, PubMed: 33542083
Corr, PAG. j. (2004). Reinforcement sensitivity theory and personality.
Neurociencia & Revisiones de biocomportamiento, 28(3), 317–332. https://
doi.org/10.1016/j.neubiorev.2004.01.005, PubMed: 15225974
Costa, PAG. T., & McCrae, R. R. (1992). Four ways five factors are
basic. Personality and Individual Differences, 13(6), 653–665.
https://doi.org/10.1016/0191-8869(92)90236-I
Cremers, h. r., Apostar, t. D., & Yarkoni, t. (2017). The relation
between statistical power and inference in fMRI. PLoS ONE,
12(11), e0184923. https://doi.org/10.1371/journal.pone.0184923,
PubMed: 29155843
Neurociencia en red
641
yo
D
oh
w
norte
oh
a
d
mi
d
F
r
oh
metro
h
t
t
pag
:
/
/
d
i
r
mi
C
t
.
metro
i
t
.
/
t
/
mi
d
tu
norte
mi
norte
a
r
t
i
C
mi
–
pag
d
yo
F
/
/
/
/
/
5
3
6
3
1
1
9
6
0
4
7
7
norte
mi
norte
_
a
_
0
0
1
9
8
pag
d
t
.
F
b
y
gramo
tu
mi
s
t
t
oh
norte
0
8
S
mi
pag
mi
metro
b
mi
r
2
0
2
3
Personality network neuroscience
Demetriou, l., Kowalczyk, oh. S., Tyson, GRAMO., Bello, T., Newbould,
R. D., & Wall, METRO. B. (2018). A comprehensive evaluation of increas-
ing temporal resolution with multiband-accelerated protocols and
effects on statistical outcome measures in fMRI. NeuroImagen, 176,
404–416. https://doi.org/10.1016/j.neuroimage.2018.05.011,
PubMed: 29738911
DeYoung, C. GRAMO. (2010). Personality neuroscience and the biology
of traits. Social and Personality Psychology Compass, 4(12),
1165–1180. https://doi.org/10.1111/j.1751-9004.2010.00327.x
Dubois, J., Galdi, PAG., Pablo, l. K., & Adolphs, R. (2018). A distributed
brain network predicts general intelligence from resting-state
human neuroimaging data. Philosophical Transactions of the
Royal Society B: Ciencias Biologicas, 373(1756), 20170284.
https://doi.org/10.1098/rstb.2017.0284, PubMed: 30104429
Duek, o., Spiller, t. r., Pietrzak, R. h., Frito, mi. I., & Harpaz-
Rotem, I. (2020). Network analysis of PTSD and depressive
symptoms in 158,139 treatment-seeking veterans with PTSD.
Depression and Anxiety, 1–9. https://doi.org/10.1002/da.23112,
PubMed: 33190348
Esfahlani, F. Z., Jo, y., Faskowitz, J., Byrge, l., Kennedy, D., despreciar, o.,
& Betzel, R. (2020). High-amplitude co-fluctuations in cortical ac-
tivity drive functional connectivity. PNAS, 117(45), 28393–28401.
https://doi.org/10.1073/pnas.2005531117, PubMed: 33093200
Eysenck, h. j. (1947). Dimensions of personality. Piscataway, Nueva Jersey:
Editores de transacciones.
Faskowitz, J., Esfahlani, F. Z., Jo, y., despreciar, o., & Betzel, R. F.
(2019). Edge-centric functional network representations of
human cerebral cortex reveal overlapping system-level architec-
tura. BioRxiv, 799924. https://doi.org/10.1101/799924
Finn, mi. S., shen, X., Scheinost, D., Rosenberg, METRO. D., Huang, J.,
Chun, METRO. METRO., Papademetris, X., & Constable, R. t. (2015).
Functional connectome fingerprinting: Identifying individuals
using patterns of brain connectivity. Neurociencia de la naturaleza, 18(11),
1664–1671. https://doi.org/10.1038/nn.4135, PubMed: 26457551
Finn, mi. S., Glerean, MI., Khojandi, A. y., Nielson, D., Molfese, PAG. J.,
Handwerker, D. A., & Bandettini, PAG. A. (2020). Idiosynchrony:
From shared responses to individual differences during naturalistic
neuroimaging. NeuroImagen, 215, 116828. https://doi.org/10
.1016/j.neuroimage.2020.116828, PubMed: 32276065
Furnham, A. (1986). Response bias, social desirability and dissimu-
lación. Personality and Individual Differences, 7(3), 385–400.
https://doi.org/10.1016/0191-8869(86)90014-0
Ginestet, C. MI., Nichols, t. MI., bullmore, mi. T., & Simmons, A.
(2011). Brain network analysis: Separating cost from topology
using cost-integration. PloS ONE, 6(7), e21570. https://doi.org
/10.1371/diario.pone.0021570, PubMed: 21829437
Goldberg, l. R. (1990). An alternative “description of personality”:
The Big-Five Factor Structure. Journal of Personality and Social
Psicología, 59(6), 1216–1229. https://doi.org/10.1037/0022
-3514.59.6.1216, PubMed: 2283588
gordon, mi. METRO., Laumann, t. o., Esperemos, B., Huckins, j. F.,
kelly, W.. METRO., & Petersen, S. mi. (2016). Generation and evalua-
tion of a cortical area parcellation from resting-state correlations.
Corteza cerebral, 26(1), 288–303. https://doi.org/10.1093/cercor
/bhu239, PubMed: 25316338
graton, C., Laumann, t. o., Nielsen, A. NORTE., verde, D. J., gordon,
mi. METRO., Gilmore, A. w., nelson, S. METRO., carbonero, R. S., Snyder,
A. Z., Schlaggar, B. l., Dösenbach, NORTE., & Petersen, S. mi.
(2018). Functional brain networks are dominated by stable group
and individual factors, not cognitive or daily variation. Neurona,
98(2), 439–452. https://doi.org/10.1016/j.neuron.2018.03.035,
PubMed: 29673485
Gray, j. A. (1972). Learning theory, the conceptual nervous system
and personality. In V. D. Nebylitsyn & j. A. Gray (Editores.), Biológico
bases of individual behavior (páginas. 372–399). Nueva York: Académico
Prensa. https://doi.org/10.1016/B978-0-12-515350-8.50030-2
Gray, j. A., & McNaughton, norte. (2000). The neuropsychology of
ansiedad: An enquiry into the functions of the septo-hippocampal
sistema (2y ed.). Oxford: prensa de la Universidad de Oxford.
verde, A. S., gao, S., Noble, S., Scheinost, D., & Constable, R. t.
(2020). How tasks change whole-brain functional organization to
reveal brain-phenotype relationships. Informes celulares, 32(8), 108066.
https://doi.org/10.1016/j.celrep.2020.108066, PubMed: 32846124
Gruskin, D. C., Rosenberg, METRO. D., & holmes, A. j. (2020).
Relationships between depressive symptoms and brain responses
during emotional movie viewing emerge in adolescence.
NeuroImagen, 216, 116217. https://doi.org/10.1016/j.neuroimage
.2019.116217, PubMed: 31628982
Guilford, j. PAG. (1959). Personality. Nueva York: McGraw-Hill.
Hagmann, PAG. (2005). From diffusion MRI to brain connectomics
[PhD thesis, Université de Lausanne]. https://biblion.epfl.ch
/EPFL/theses/2005/3230/EPFL_TH3230.pdf
Hallquist, METRO. NORTE., & Hillary, F. GRAMO. (2018). Graph theory approaches
to functional network organization in brain disorders: A critique
for a brave new small-world. Neurociencia en red, 3(1), 1–26.
https://doi.org/10.1162/netn_a_00054, PubMed: 30793071
Hebb, D. oh. (1955). Drives and the C. norte. S. (conceptual nervous
sistema). Revisión psicológica, 62(4), 243–254. https://doi.org
/10.1037/h0041823, PubMed: 14395368
Hilger, K., Ekman, METRO., Fiebach, C. J., & Basten, Ud.. (2017).
Intelligence is associated with the modular structure of intrinsic
redes cerebrales. Informes Científicos, 7(1), 16088. https://doi.org/10
.1038/s41598-017-15795-7, PubMed: 29167455
Hofmann, S. GRAMO., Curtiss, j. MI., & Hayes, S. C. (2020). Beyond linear
mediation: Toward a dynamic network approach to study treat-
ment processes. Clinical Psychology Review, 76, 101824. https://
doi.org/10.1016/j.cpr.2020.101824, PubMed: 32035297
hsu, W.. T., Rosenberg, METRO. D., Scheinost, D., Constable, R. T., &
Chun, METRO. METRO. (2018). Resting-state functional connectivity predicts
neuroticism and extraversion in novel individuals. Social Cognitive
and Affective Neuroscience, 13(2), 224–232. https://doi.org/10
.1093/scan/nsy002, PubMed: 29373729
Jiang, r., Calhoun, V. D., Zuo, NORTE., lin, D., li, J., Admirador, l., chi, S., Sol,
h., Fu, Z., Song, METRO., Jiang, T., & Sui, j. (2018). Connectome-
based individualized prediction of temperament trait scores.
NeuroImagen, 183, 366–374. https://doi.org/10.1016/j
.neuroimage.2018.08.038, PubMed: 30125712
John, oh. PAG., & Srivastava, S. (1999). The Big-Five trait taxonomy:
Historia, medición, and theoretical perspectives. en l. A.
Pervin & oh. PAG. John (Editores.), Handbook of personality: Teoría
and research (volumen. 2, páginas. 102–138). Nueva York: Guilford Press.
Kosinski, METRO., Stillwell, D., & Graepel, t. (2013). Private traits and
attributes are predictable from digital records of human behavior.
procedimientos de la Academia Nacional de Ciencias, 110(15),
5802–5805. https://doi.org/10.1073/pnas.1218772110,
PubMed: 23479631
Neurociencia en red
642
yo
D
oh
w
norte
oh
a
d
mi
d
F
r
oh
metro
h
t
t
pag
:
/
/
d
i
r
mi
C
t
.
metro
i
t
.
/
/
t
mi
d
tu
norte
mi
norte
a
r
t
i
C
mi
–
pag
d
yo
F
/
/
/
/
/
5
3
6
3
1
1
9
6
0
4
7
7
norte
mi
norte
_
a
_
0
0
1
9
8
pag
d
.
t
F
b
y
gramo
tu
mi
s
t
t
oh
norte
0
8
S
mi
pag
mi
metro
b
mi
r
2
0
2
3
Personality network neuroscience
krüger, R. F., & Markon, k. mi. (2006). Understanding psychopa-
thology. Current Directions in Psychological Science, 15(3),
113–117. https://doi.org/10.1111/j.0963-7214.2006.00418.X,
PubMed: 18392116
Kumar, K., Toews, METRO., Chauvin, l., Colliot, o., & Desrosiers, C.
(2018). Multi-modal brain fingerprinting: A manifold approxima-
tion based framework. NeuroImagen, 183, 212–226. https://doi
.org/10.1016/j.neuroimage.2018.08.006, PubMed: 30099077
Laird, A. r., Lancaster, j. l., & Fox, PAG. t. (2005). BrainMap: The Social
evolution of a human brain mapping database. Neuroinformatics,
3(1), 065–078. https://doi.org/10.1385/NI:3:1:065
Langer, NORTE., Pedroni, A., Gianotti, l. r., Hänggi, J., Knoch, D., &
Jäncke, l. (2012). Functional brain network efficiency predicts
intelligence. Mapeo del cerebro humano, 33(6), 1393–1406. https://
doi.org/10.1002/hbm.21297, PubMed: 21557387
Sotavento, K., & Ashton, METRO. C. (2004). Psychometric properties of the
HEXACO personality inventory. Multivariate Behavioral Research,
39(2), 329–358. https://doi.org/10.1207/s15327906mbr3902_8,
PubMed: 26804579
Levakov, GRAMO., Faskowitz, J., Avaro, GRAMO., & despreciar, oh. (2021).
Mapping structure to function and behavior with individual-level
connectome embedding. bioRxiv. https://doi.org/10.1101/2021
.01.13.426513
Liu, w., Kohn, NORTE., & Fernández, GRAMO. (2019). Intersubject similarity of
personality is associated with intersubject similarity of brain con-
nectivity patterns. NeuroImagen, 186, 56–69. https://doi.org/10
.1016/j.neuroimage.2018.10.062, PubMed: 30389630
Marek, S., Tervo-Clemmens, B., Calabro, F. J., Montez, D. F., kay, B. PAG.,
Hatoum, A. S., Donohue, METRO. r., Foran W., Molinero, R. l., Feczko, MI.,
Miranda-Dominguez, o., graham, A. METRO., Earl, mi. A., Perrone, A. J.,
Cordova, METRO., Doyle, o., moore, l. A., Conan, GRAMO., Uriarte, J., Snider,
K., Tam, A., Chen, J., Newbold, D. J., … Dosenbach, norte. Ud.. (2020).
Towards reproducible brain-wide association studies. BioRxiv.
https://doi.org/10.1101/2020.08.21.257758
Marengo, D., & Montag, C. (2020). Digital phenotyping of big five
personality via Facebook data mining: A meta-analysis. Digital
Psicología, 1(1), 52–64. https://doi.org/10.24989/dp.v1i1.1823
Markett, S., Montag, C., Melchers, METRO., Weber, B., & Reuter, METRO.
(2016). Anxious personality and functional efficiency of the
insular-opercular network: A graph-analytic approach to resting-
state fMRI. Cognitivo, Afectivo, & Neurociencia conductual.
https://doi.org/10.3758/s13415-016-0451-2, PubMed: 27515174
Markett, S., Montag, C., & Reuter, METRO. (2014). In favor of behavior:
On the importance of experimental paradigms in testing predictions
from Gray’s revised reinforcement sensitivity theory. Fronteras en
System Neuroscience, 8. https://doi.org/10.3389/fnsys.2014
.00184, PubMed: 25324737
Markett, S., Wudarczyk, oh. A., Biswal, B. B., Jawinski, PAG., & Montag, C.
(2018). Affective Network Neuroscience. Frontiers in Neuroscience,
12. https://doi.org/10.3389/fnins.2018.00895, PubMed: 30618543
Medalla, j. D., Pasqualetti, F., hamilton, R. h., Thompson-Schill,
S. l., & bassett, D. S. (2017). Brain and cognitive reserve: Translation
via network control theory. Neurociencia & Revisiones de biocomportamiento,
75, 53–64. https://doi.org/10.1016/j.neubiorev.2017.01.016,
PubMed: 28104411
Mobbs, D., Petrovic, PAG., Marchant, j. l., Hassabis, D., Weiskopf, NORTE.,
Seymour, B., Dolan, R. J., & Frith, C. D. (2007). When fear is near:
Threat imminence elicits prefrontal-periaqueductal gray shifts in
humanos. Ciencia, 317(5841), 1079–1083. https://doi.org/10
.1126/science.1144298, PubMed: 17717184
Montag, C., Ebstein, R. PAG., Jawinski, PAG., & Markett, S. (2020).
Molecular genetics in psychology and personality neuroscience:
On candidate genes, genome wide scans, and new research strat-
egies. Neurociencia & Revisiones de biocomportamiento, 118, 163–174.
https://doi.org/10.1016/j.neubiorev.2020.06.020, PubMed:
32681937
Mueller, S., Wang, D., Fox, METRO. D., Cacerola, r., Lu, J., li, K., Sol, w.,
Buckner, R. l., Liu, h., (2015). Reliability correction for functional
conectividad: Theory and implementation. Mapeo del cerebro humano,
36, 4664–4680. https://doi.org/10.1002/hbm.22947, PubMed:
26493163
Mulders, PAG., Llera, A., Tendolkar, I., van Eijndhoven, PAG., &
beckman, C. (2018). Personality profiles are associated with
functional brain networks related to cognition and emotion.
Informes Científicos, 8(1), 1–8. https://doi.org/10.1038/s41598
-018-32248-X, PubMed: 30224828
Nguyen, METRO., Vanderwal, T., & Hasson, Ud.. (2019). Shared under-
standing of narratives is correlated with shared neural responses.
NeuroImagen, 184, 161–170. https://doi.org/10.1016/j
.neuroimage.2018.09.010, PubMed: 30217543
Noble, S., Scheinost, D., & Constable, R. t. (2019). A decade of
test-retest reliability of functional connectivity: A systematic
review and meta-analysis. NeuroImagen, 203, 116157. https://doi
.org/10.1016/j.neuroimage.2019.116157, PubMed: 31494250
Nooner, k. B., Colcombe, S., Tobe, r., Mennes, METRO., Benedict, METRO.,
Moreno, A., Panek, l. J., Marrón, S., Zavitz, S. T., li, P., Sikka, S.,
Gutman, D., Bangaru, S., Schlachter, R. T., Kamiel, S. METRO., Anwar,
A. r., Hinz, C. METRO., Kaplan, METRO. S., Rachlin, A. B., Adelsberg, S., …
Milham, METRO. (2012). The NKI-Rockland sample: A model for
accelerating the pace of discovery science in psychiatry.
Frontiers in Neuroscience, 6, 152. https://doi.org/10.3389/fnins
.2012.00152, PubMed: 23087608
Okbay, A., Baselmans, B. METRO. l., De Neve, J.-E., Turley, PAG., Nivard,
METRO. GRAMO., Fontana, METRO. A., Meddens, S. F. w., Linnér, R. K., Rietveld,
C. A., Derringer, J., Gratten, J., Sotavento, j. J., Liu, j. Z., de Vlaming, r.,
Ahluwalia, t. S., Buchwald, J., Cavadino, A., Frazier-Wood,
A. C., Furlotte, norte. A., … Cesarini, D. (2016). Genetic variants
associated with subjective well-being, depressive symptoms,
and neuroticism identified through genome-wide analyses.
Nature Genetics, 48(6), 624–633. https://doi.org/10.1038/ng
.3552, PubMed: 27089181
Panksepp, J., Knutson, B., & Pruitt, D. l. (1998). Toward a neuro-
science of emotion. In What develops in emotional develop-
mento? (páginas. 53–84). Bostón, MAMÁ: Saltador. https://doi.org/10
.1007/978-1-4899-1939-7_3
parque, l., Fulcher, B., Yücel, METRO., & Proporcionó, A. (2018). An evaluation
of the efficacy, fiabilidad, and sensitivity of motion correction
strategies for resting-state functional MRI. NeuroImagen, 171,
415–436. https://doi.org/10.1016/j.neuroimage.2017.12.073,
PubMed: 29278773
Perkins, A. METRO., Ettinger, Ud., davis, r., Foster, r., williams, S. C. r.,
& Corr, PAG. j. (2009). Effects of lorazepam and citalopram on hu-
man defensive reactions: Ethopharmacological differentiation of
fear and anxiety. Revista de neurociencia, 29(40), 12617–12624.
https://doi.org/10.1523/ JNEUROSCI.2696-09.2009, PubMed:
19812336
Neurociencia en red
643
yo
D
oh
w
norte
oh
a
d
mi
d
F
r
oh
metro
h
t
t
pag
:
/
/
d
i
r
mi
C
t
.
metro
i
t
.
/
/
t
mi
d
tu
norte
mi
norte
a
r
t
i
C
mi
–
pag
d
yo
F
/
/
/
/
/
5
3
6
3
1
1
9
6
0
4
7
7
norte
mi
norte
_
a
_
0
0
1
9
8
pag
d
t
.
F
b
y
gramo
tu
mi
s
t
t
oh
norte
0
8
S
mi
pag
mi
metro
b
mi
r
2
0
2
3
Personality network neuroscience
Perkins, A. METRO., Kemp, S. MI., & Corr, PAG. j. (2007). Fear and anxiety as
separable emotions: An investigation of the revised reinforcement
sensitivity theory of personality. Emoción, 7(2), 252–261. https://
doi.org/10.1037/1528-3542.7.2.252, PubMed: 17516804
Perkins, A. METRO., Strawbridge, r., Arnone, D., williams, S. C. r.,
Gasston, D., Cleare, A. J., O’Daly, o., Kumari, v., Ettinger, Ud.,
& Corr, PAG. j. (2019). Towards a neuroscience-based theory of
personality: Within-subjects dissociation of human brain activity
during pursuit and goal conflict. Personality Neuroscience, 2.
https://doi.org/10.1017/pen.2019.2, PubMed: 32435739
Pickering, A. D. (1997). The conceptual nervous system and person-
ality: From Pavlov to neural networks. European Psychologist,
2(2), 139–163. https://doi.org/10.1027/1016-9040.2.2.139
Privado, J., Román, F. J., Saénz-Urturi, C., Burgaleta, METRO., & Colom, R.
(2017). Gray and white matter correlates of the Big Five personality
traits. Neurociencia, 349, 174–184. https://doi.org/10.1016/j
.neuroscience.2017.02.039, PubMed: 28259799
Pruim, R. h., Mennes, METRO., Buitelaar, j. K., & beckman, C. F.
(2015). Evaluation of ICA-AROMA and alternative strategies for
motion artifact removal in resting state fMRI. NeuroImagen, 112,
278–287. https://doi.org/10.1016/j.neuroimage.2015.02.063,
PubMed: 25770990
Rhoads, S. A., Cardinale, mi. METRO., O’Connell, K., Palmer, A. l.,
VanMeter, j. w., & Marsh, A. A. (2020). Mapping neural activity
patterns to contextualized fearful facial expressions onto callous-
unemotional (CU) traits: Intersubject representational similarity
analysis reveals less variation among high-CU adolescents.
Personality Neuroscience, 3, e20. https://doi.org/10.1017/pen
.2020.13, PubMed: 33283146
Rubinov, METRO., & despreciar, oh. (2010). Complex network measures of
conectividad cerebral: Uses and interpretations. NeuroImagen, 52(3),
1059–1069. https://doi.org/10.1016/j.neuroimage.2009.10.003,
PubMed: 19819337
Salehi, METRO., verde, A. S., Karbasi, A., shen, X., Scheinost, D., &
Constable, R. t. (2020). There is no single functional atlas even
for a single individual: Functional parcel definitions change with
tarea. NeuroImagen, 208, 116366. https://doi.org/10.1016/j
.neuroimage.2019.116366, PubMed: 31740342
Satterthwaite, t. D., eliot, METRO. A., Gerraty, R. T., Ruparel, K.,
Loughead, J., Calkins, METRO. MI., Eickhoff, S. B., Hakonarson, h.,
Gur, R. C., Gur, R. MI., & Lobo, D. h. (2013). An improved frame-
work for confound regression and filtering for control of motion
artifact in the preprocessing of resting-state functional connectivity
datos. NeuroImagen 64, 240–256. https://doi.org/10.1016/j
.neuroimage.2012.08.052, PubMed: 22926292
Schaefer, A., kong, r., gordon, mi. METRO., Laumann, t. o., Zuo, X. NORTE.,
holmes, A. J., Eickhoff, S. B., & yo, B. t. (2018). Local-global
parcellation of the human cerebral cortex from intrinsic functional
connectivity MRI. Corteza cerebral, 28(9), 3095–3114. https://doi
.org/10.1093/cercor/bhx179, PubMed: 28981612
Seitzman, B. A., graton, C., Laumann, t. o., gordon, mi. METRO.,
Esperemos, B., Dworetsky, A., Kraus, B. T., Gilmore, A. w., Iceberg,
j. J., Ortega, METRO., Nguyen, A., verde, D. J., McDermott, k. B.,
nelson, S. METRO., Lessov-Schlaggar, C. NORTE., Schlaggar, B. l.,
Dösenbach, norte. Ud.. F., & Petersen, S. mi. (2019). Trait-like variants
in human functional brain networks. Actas del Nacional
Academia de Ciencias, 116(45), 22851–22861. https://doi.org/10
.1073/pnas.1902932116, PubMed: 31611415
despreciar, o., Tononi, GRAMO., & kötter, R. (2005). The human connectome:
A structural description of the human brain. PLoS computacional
Biología, 1(4), e42. https://doi.org/10.1371/journal.pcbi.0010042,
PubMed: 16201007
despreciar, o., Faskowitz, J., Teixera, S., & Betzel, R. (2020). Dynamic
expression of brain functional systems disclosed by fine-scale
analysis of edge time series. BioRxiv, 2020.08.23.263541. https://
doi.org/10.1101/2020.08.23.263541
Stachl, C., Au, P., Schoedel, r., Gosling, S. D., Harari, GRAMO. METRO., Buschek,
D., Völkel, S. T., Schuwerk, T., Oldemeier, METRO., Ullmann, T.,
Hussmann, h., Bischl, B., & Bühner, METRO. (2020). Predicting per-
sonality from patterns of behavior collected with smartphones.
procedimientos de la Academia Nacional de Ciencias, 117(30),
17680–17687. https://doi.org/10.1073/pnas.1920484117,
PubMed: 32665436
Sui, J., Jiang, r., Bustillo, J., & Calhoun, V. (2020). Neuroimaging-
based individualized prediction of cognition and behavior for
mental disorders and health: Methods and promises. Biológico
Psiquiatría, 88(11), 818–828. https://doi.org/10.1016/j.biopsych
.2020.02.016, PubMed: 32336400
taylor, S., Landry, C. A., Paluszek, METRO. METRO., Fergus, t. A., McKay, D., &
Asmundson, GRAMO. j. GRAMO. (2020). COVID stress syndrome: Concept,
estructura, and correlates. Depression and Anxiety, 37(8), 706–714.
https://doi.org/10.1002/da.23071, PubMed: 32627255
telesford, q. K., Burdette, j. h., & Laurienti, PAG. j. (2013). An explo-
ration of graph metric reproducibility in complex brain networks.
Frontiers in Neuroscience, 7, 1–9. https://doi.org/10.3389/fnins
.2013.00067, PubMed: 23717257
Termenon, METRO., Jaillard, A., Delon-Martin, C., & Achard, S. (2016).
Reliability of graph analysis of resting state fMRI using test-retest
dataset from the Human Connectome Project. NeuroImagen, 142,
172–187. https://doi.org/10.1016/j.neuroimage.2016.05.062,
PubMed: 27282475
Tompson, S. h., Falk, mi. B., Vettel, j. METRO., & bassett, D. S. (2018).
Network approaches to understand individual differences in
conectividad cerebral: opportunities for personality neuroscience.
Personality Neuroscience, 1, e5. https://doi.org/10.1017/pen
.2018.4, PubMed: 30221246
Toschi, NORTE., Riccelli, r., Indovina, I., Terracciano, A., & Passamonti,
l. (2018). Functional connectome of the five-factor model of per-
sonality. Personality Neuroscience, 1, e8. https://doi.org/10.1017
/pen.2017.2, PubMed: 30294715
van Baar, j. METRO., Chang, l. J., & Sanfey, A. GRAMO. (2019). The compu-
tational and neural substrates of moral strategies in social
Toma de decisiones. Comunicaciones de la naturaleza, 10(1), 1–14. https://
doi.org/10.1038/s41467-019-09161-6, PubMed: 30940815
van den heuvel, METRO. PAG., el largo, S. C., Brilla, A., Seguin, C.,
yo, B. T., & Schmidt, R. (2017). Proportional thresholding in
resting-state fMRI functional connectivity networks and conse-
quences for patient-control connectome studies: Issues and rec-
ommendations. NeuroImagen, 152, 437–449. https://doi.org/10
.1016/j.neuroimage.2017.02.005, PubMed: 28167349
Van Wijk, B. C., estampar, C. J., & Daffertshofer, A. (2010). Comparing
brain networks of different size and connectivity density using
graph theory. PLoS ONE, 5(10), e13701. https://doi.org/10.1371
/diario.pone.0013701, PubMed: 21060892
Vul, MI., harris, C., Winkielman, PAG., & Pashler, h. (2009). Puzzlingly
high correlations in fMRI studies of emotion, personality, and social
Neurociencia en red
644
yo
D
oh
w
norte
oh
a
d
mi
d
F
r
oh
metro
h
t
t
pag
:
/
/
d
i
r
mi
C
t
.
metro
i
t
.
/
/
t
mi
d
tu
norte
mi
norte
a
r
t
i
C
mi
–
pag
d
yo
F
/
/
/
/
/
5
3
6
3
1
1
9
6
0
4
7
7
norte
mi
norte
_
a
_
0
0
1
9
8
pag
d
.
t
F
b
y
gramo
tu
mi
s
t
t
oh
norte
0
8
S
mi
pag
mi
metro
b
mi
r
2
0
2
3
Personality network neuroscience
cognition. Perspectives on Psychological Science, 4(3), 274–290.
https://doi.org/10.1111/j.1745-6924.2009.01125.x, PubMed:
26158964
Yarkoni, T., Poldrack, R. A., Nichols, t. MI., VanEssen, D. C., &
Apostar, t. D. (2011). Large-scale automated synthesis of human
functional neuroimaging data. Nature Methods, 8(8), 665–670.
https://doi.org/10.1038/nmeth.1635, PubMed: 21706013
yan, C. GRAMO., Craddock, R. C., Él, y., & Milham, METRO. PAG. (2013).
Addressing head motion dependencies for small-world topologies
in functional connectomics. Frontiers in Human Neuroscience,
7, 910. https://doi.org/10.3389/fnhum.2013.00910, PubMed:
24421764
Brilla, A., Proporcionó, A., Harding, I. h., cocineros, l., Yücel, METRO., Pantelis, C.,
& bullmore, mi. t. (2010). Whole-brain anatomical networks: Hace
the choice of nodes matter? NeuroImagen, 50(3), 970–983. https://
doi.org/10.1016/j.neuroimage.2009.12.027, PubMed: 20035887
Ziegelstein, R. (2017). Personomics: The missing link in the evolu-
tion from precision medicine to personalized medicine. Diario
of Personalized Medicine, 7(4), 11. https://doi.org/10.3390
/jpm7040011, PubMed: 29035320
yo
D
oh
w
norte
oh
a
d
mi
d
F
r
oh
metro
h
t
t
pag
:
/
/
d
i
r
mi
C
t
.
metro
i
t
.
t
/
/
mi
d
tu
norte
mi
norte
a
r
t
i
C
mi
–
pag
d
yo
F
/
/
/
/
/
5
3
6
3
1
1
9
6
0
4
7
7
norte
mi
norte
_
a
_
0
0
1
9
8
pag
d
t
.
F
b
y
gramo
tu
mi
s
t
t
oh
norte
0
8
S
mi
pag
mi
metro
b
mi
r
2
0
2
3
Neurociencia en red
645
