The Structural Neuroanatomy of Metacognitive
Insight in Schizophrenia and Its Psychopathological
and Neuropsychological Correlates
Gianfranco Spalletta,1* Fabrizio Piras,1Federica Piras,1
Carlo Caltagirone,1,2and Maria Donata Orfei1
1Department of Clinical and Behavioural Neurology, Neuropsychiatry Laboratory, IRCCS
Santa Lucia Foundation, Rome, Italy
2Neuroscience Department, Tor Vergata University, Rome, Italy
Abstract: Lack of insight into illness is a multidimensional phenomenon that has relevant implications
on clinical course and therapy compliance. Here, we focused on metacognitive insight in schizophre-
nia, that is, the ability to monitor one’s changes in state of mind and sensations, with the aim of inves-
tigating its neuroanatomical, psychopathological, and neuropsychological correlates. Fifty-seven
consecutive patients with Diagnostic and Statistical Manual of Mental Disorders (Fourth Edition, Text
Revision) diagnosis of schizophrenia were administered the Insight Scale, and comprehensive psycho-
pathological and neuropsychological batteries. They underwent a high-resolution T1-weighted mag-
netic resonance imaging investigation. Gray matter (GM) and white matter (WM) volumes were
analyzed on a voxel-by-voxel basis using Statistical Parametric Mapping 8. Reduced metacognitive
insight was related to reduced GM volumes in the left ventrolateral prefrontal cortex, right dorsolateral
prefrontal cortex and insula, and bilateral premotor area and putamen. Further, it was related to
reduced WM volumes of the right superior longitudinal fasciculum, left corona radiata, left forceps
minor, and bilateral cingulum. Increased metacognitive insight was related to increased depression
severity and attentional control impairment, while the latter was related to increased GM volumes in
brain areas linked to metacognitive insight. Results of this study suggest that prefrontal GM and WM
bundles, all implied in cognitive control and self-reflection, may be the neuroanatomical correlates of
metacognitive insight in schizophrenia. Further, higher metacognitive insight is hypothesized to be a
risk factor for depression which may subsequently impair attention. This line of research may provide
the basis for the development of cognitive interventions aimed at improving self-monitoring and com-
pliance to treatment. Hum Brain Mapp 00:000–000, 2014. V
C 2014 Wiley Periodicals, Inc.
Key words: awareness; self-monitoring; prefrontal cortex; white matter; cognition; psychosis
Contract grant sponsor: Italian Ministry of Health; Contract grant
numbers: RC08A, RC09A, RC10A, RC11A, and RC12A.
*Correspondence to: Gianfranco Spalletta, Neuropsychiatry Labo-
ratory, Department of Clinical and Behavioral Neurology, IRCCS
Santa Lucia Foundation, Via Ardeatina, 306, 00179 Rome, Italy.
E-mail: g.spalletta@hsan talucia.it
Received for publication 15 November 2013; Revised 17 February
2014; Accepted 25 February 2014.
Published online 00 Month 2014 in Wiley Online Library
r Human Brain Mapping 00:00–00 (2014) r
C 2014 Wiley Periodicals, Inc.
Insight into illness in clinical psychiatry is defined as
the awareness to be ill and changed plus the ability to
evaluate the causes and the severity of illness (Jaspers,
1963). Lack of insight is a key feature in schizophrenia
spectrum disorders and gained growing attention in a clin-
ical perspective due to its incidence in the psychiatric pop-
ulation, which is estimated to range between 50 and 80%
(Amador and Gorman, 1998). Given its detrimental impact
on relapses, number of hospitalizations, compliance to
treatment and symptom remission, impaired insight may
have important consequences for quality of life and func-
tioning in a work setting (Boyer et al., 2012; Erickson et al.,
2011; Kurtz and Tolman, 2011; Mohamed et al., 2009).
Insight isa multidimensional
implies several facets at least partially independent from
each other (David et al., 2012; Orfei et al., 2008). A classical
approach focuses on clinical insight (Amador and Strauss,
1993; David, 1990), that is awareness of illness and the
capacity to relabel symptoms as pathological phenomena.
At the beginning of 2000, Beck et al. (2004) developed the
construct of cognitive insight, which reflects the ability to
monitor one’s ongoing mental activities. Thus, cognitive
insight expresses a general cognitive attitude of informa-
tion processing, rather than being related specifically to
awareness of illness in schizophrenia. In the same years,
Markov? a and Berrios (1992) and Markov? a et al. (2003)
challenged an innovative approach to the issue, focusing
on metacognitive processes applied specifically to aware-
ness of illness. Metacognition is a high-order cognitive
function which consists in one’s knowledge concerning
one’s cognitive processes and products or anything related
to them, such as perception, memory, problem solving
and behavior (Flavell, 1979).
The authors stemmed from the assumption that when
individuals become mentally unwell, a number of cogni-
tive and experiential changes occur affecting perception of
self, of one’s environment and of the interaction between
these (Markov? a et al., 2003). In this perspective, insight
monitoring of such changes in subjective self-experience
and the ability to feel and express the related emotional
unease in patients affected by schizophrenia. Given these
considerations, in the present article from now on we will
refer to this specific dimension of self-awareness as meta-
cognitive insight. Theinvestigation
insight gives the advantage to be less susceptible to self
deception or simulation than clinical insight. In fact, while
clinical insight does not imply necessarily any actual sig-
nificant change in patients’ underlying belief system nor in
one’s own behavior (Bora et al., 2007; Jaspers, 1963), meta-
cognitive insight best reflects one’s actual judgments about
the self. The Insight Scale (IS), a self-rated questionnaire,
was developed to measure such construct. While several
studies have described the neural correlates of clinical
insight, reporting an involvement of prefrontal areas
aform of ongoing self-
(Cooke et al., 2008; Flashman et al., 2001; Laroi et al., 2000;
Morgan et al., 2010; Sapara et al., 2007; Shad et al., 2004,
2006), temporal areas (Cooke et al., 2008; Ha et al., 2004;
Palaniyappan et al., 2011), or even no structural alterations
accompanying poor clinical insight (Bassitt et al., 2007;
Buchy et al., 2009; Rossell et al., 2003), only few studies
investigated the neural correlates of cognitive insight. Such
studies highlighted the involvement of the ventrolateral
prefrontal area (Orfei et al., 2013), the hippocampus
(Buchy et al., 2010), and the fornix (Buchy et al., 2012).
However, to our knowledge, no investigation on the
brain areas involved in metacognitive insight has been car-
ried out so far.
Thus, the main aim of the present study was to investi-
gate the gray matter (GM) and white matter (WM) neuroa-
natomical correlates of metacognitive insight, using a
volumetric neuroimaging approach. In order to do that,
the IS was administered to a sample of patients with diag-
nosis of schizophrenia, and the neuroanatomical correlates
of the IS score were explored by performing whole brain
GM and WM volumetric analyses. Given the well-
established involvement of brain prefrontal areas in self-
reflection and self-monitoring tasks (Burianova and Grady,
2007; Liemburg et al., 2012; Orfei et al., 2013), we hypothe-
sized that increased metacognitive insight would be
related to: (a) GM volume of ventral and dorsal lateral
prefrontal cortices (VLPFC and DLPFC) and (b) WM vol-
ume of fascicules connecting prefrontal areas. Secondarily,
we explored also the psychopathological and neuropsy-
chological correlates of metacognitive insight.
We recruited 75 consecutive outpatients diagnosed with
schizophrenia according to the Diagnostic and Statistical
Manual of Mental Disorders (Fourth Edition, Text Revi-
sion; DSM-IV-TR; APA, 2000) criteria. All patients were
diagnosed by one senior clinical psychiatrist (GS) using
the structured clinical interview for DSM-IV-TR (SCID-I/P;
First et al., 2002). Other inclusion criteria were (1) age
between 18 and 65 years; (2) at least 8 years of education;
(3) no dementia or cognitive deterioration according to the
DSM-IV-TR criteria, and a Mini-Mental State Examination
(MMSE; Folstein et al., 1975) score higher than 24, consist-
ent with normative data in the Italian population (Measso
et al., 1993); and (4) suitability for magnetic resonance
imaging (MRI) scan. Exclusion criteria were (1) a history
of alcohol or drug dependence or abuse in the last two
years according to the DSM-IV-TR diagnostic criteria, (2) a
history of traumatic head injury, (3) any past or present
major medical or neurological illness, (4) any other psychi-
atric disorder or mental retardation diagnosis, and (5) MRI
evidence of focal parenchymal abnormalities or cerebro-
vascular diseases. In particular, the presence, severity, and
rSpalletta et al. r
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location of vascular lesions were rated according to a pro-
tocol designed for the Rotterdam Scan Study. Generally,
they are considered present in cases of hyperintense
lesions on both proton-density and T2-weighted and were
rated semiquantitatively as 0 (none), 1 (pencil-thin lining),
2 (smooth halo), or 3 (large confluent) for three separate
regions; adjacent to frontal horns (frontal caps), adjacent to
the wall of the lateral ventricles (bands), and adjacent to
the occipital horns (occipital caps). The total vascular
lesion load was calculated by adding the region-specific
scores (range, 0–9). In the present study, only patients
rated 0 were included. Of the initial sample of 75 patients,
5 were excluded for cognitive deterioration, 7 for comor-
bid substance use disorders, 5 for comorbid medical or
neurological illnesses, and 1 for previous traumatic brain
injury with lack of consciousness. Thus, the final sample
consisted of 57 outpatients. All patients were in a phase of
stable clinical compensation. Age at onset was defined as
age at first hospitalization or, when possible, age at onset
of positive or negative symptoms before the first hospitali-
zation. Extrapyramidal side effects due to current treat-
ment were assessed by the Simpson–Angus Rating Scale
(SARS; Simpson and Angus, 1970). The Abnormal Involun-
tary Movement Scale (AIMS) was used to assess tardive
dyskinesia; however, no patient suffered from this disturb-
ance. All patients were receiving stable oral doses of one
or more atypical antipsychotic drugs such as risperidone,
quetiapine, or olanzapine. Antipsychotic dosages were
converted to estimated equivalent dosages of olanzapine
by using a standard table (Woods, 2003).
Our local Ethics Committee approved the study proto-
col. Written informed consent was obtained from all
patients after they received a full explanation of the study
Psychopathological and Neuropsychological
The Positive and Negative Syndrome Scale (PANSS; Kay
et al., 1987) was administered to rate the severity of psy-
chopathological symptoms. The PANSS rates the patient
from 1 to 7 on 30 different symptoms based on the inter-
view as well as reports of family members or primary care
hospital workers. The symptoms are grouped in three
global scales, i.e., Positive Symptom Scale, Negative Symp-
tom Scale, and the General Psychopathology Scale and five
subscales, i.e., Paranoid-Belligerence, Anergia, Depression,
Activation, and Thought Disturbance. As 1, rather than 0,
is given as the lowest score for each item, a patient cannot
score lower than 30 for the total PANSS score. PANSS rat-
ings were obtained on all information available pertaining
to the last week of the assessment.
With regard to the neuropsychological assessment, to
obtain a global index of cognitive deterioration, the MMSE
was administered. This is an amply utilized neurocogni-
tive screening test measuring orientation, language, verbal
memory, attention, visuospatial function, and mental con-
trol. It is composed of 16 items, with scores ranging from 30
(no impairment) to 0 (maximum impairment). Several tests
were selected from the Mental Deterioration Battery (MDB;
Carlesimo et al., 1996) to provide information about the
functionality of different cognitive domains such as: verbal
memory [MDB Rey’s 15-word Immediate Recall (RIR) and
Delayed Recall (RDR)]; short-term visual memory (MDB
Immediate Visual Memory); logical reasoning [MDB Rav-
en’s Progressive Matrices’ 47 (PM47)]; simple constructional
praxis [MDB Copying Drawings (CD) and CD with Land-
marks (CDL)]; language (MDB Phonological Verbal Fluency
(PVF) and semantic fluency [Category Fluency test (CF);
Lucas et al., 1998]; executive functions [Modified Wisconsin
Card Sorting test (MWCST); Heaton et al., 1993]; divided
attention and attentional control [Double Barrage (DB) test].
The order of administration of all scales was the same for all
subjects. The diagnostic and psychopathological battery,
included the IS, was administered before the cognitive bat-
tery. Assessment of inter-rater reliability for raters in this
study was in the excellent to good range for all the psycho-
pathological and neuropsychological scales used, with
intraclass correlations ranging from 0.80 to 0.93. At last, the
pharmacological side effect assessment was performed.
Metacognitive Insight Assessment
The IS was originally developed in 1992 (Markov? a and
Berrios, 1992) as a 32-item questionnaire and a three-choice
answer scale (yes/no/do not know). A subsequent revision
(Markov? a et al., 2003) was realized, by deleting, adding or
rephrasing some items. The final refined version resulted in
a 30-item questionnaire, each consisting in a sentence with
which the subject is asked to agree (Yes) or not (No). When
patients agree with items relating to awareness of changes
in mental states and in their interaction with the outside
world, they are deemed as insightful and characterized by a
functional self-monitoring ability, and gain a score of 1. Oth-
erwise, if the subjects fail to detect these inner changes, they
are considered as characterized by a defective self-
monitoring resulting with a poor insight, and gaining a
score of 0. Thus, a sum score of 30 indicates full insight,
while a sum score of 0 indicates minimum insight. The IS
has proved to be valid and reliable for individuals with
schizophrenia (Markov? a et al., 2003). We administered a
validated Italian version of the IS (Orfei et al., 2007).
Image Acquisition and Processing
Participants underwent the same imaging protocol,
which included standard clinical sequences (FLAIR, DP-
T2-weighted) and a whole-brain 3D high-resolution T1-
weighted sequence, performed with a 3T Allegra MR
imager (Siemens, Erlangen, Germany). Volumetric whole-
brain T1-weighted images were obtained in the sagittal
planeusinga modifieddriven equilibriumFourier
rNeural Correlates of Metacognitive Insight r
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transform (MDEFT) sequence [echo time/repetition time-
52.4/7.92 ms, flip angle 15?, voxel-size 1 mm 3 1 mm 3
1 mm]. All planar sequence acquisitions were obtained in
the plane of the AC–PC line. Particular care was taken to
center subjects’ head in the head coil and to restrain their
movements with cushions and adhesive medical tape.
T1-weighted images were processed and examined by
using the SPM8 software (Wellcome Department of Imag-
ing Neuroscience Group, London, UK; http://www.fil.ion.
ucl.ac.uk/spm), specifically the VBM8 toolbox (http://
2007b (MathWorks, Natick, MA). The toolbox extends the
unified segmentation model (Ashburner and Friston, 2005)
consisting of MRI field intensity inhomogeneity correction,
spatial normalization and tissue segmentation at several
preprocessing steps to further improve the quality of data
preprocessing. Initially, to increase the signal-to-noise ratio
in the data, an optimized block wise nonlocal-means filter
was applied to the MRI scans using the Rician noise adap-
tion (Wiest-Daessle et al., 2008). Then, an adaptive maxi-
mum a posteriori segmentation approach extended by
partial volume estimation was employed to separate the
MRI scans into GM, WM, and cerebrospinal fluid. The seg-
mentation step was finished by applying a spatial con-
straint to the segmented tissue probability maps based on
a hidden Markow Random Field model to remove isolated
voxels which were unlikely to be a member of a certain
tissue class and to close holes in clusters of connected vox-
els of a certain class, resulting in a higher signal-to-noise
ratio of the final tissue probability maps. Then, the itera-
tive high-dimensional normalization approach provided
by the Diffeomorphic Anatomical Registration Through
Exponentiated Lie Algebra (Ashburner, 2007; DARTEL)
toolbox was applied to the segmented tissue maps in order
to register them to the stereotactic space of the Montreal
Neurological Institute (MNI). The tissue deformations
were used to modulate participants’ GM and WM tissue
maps. Voxel values of the resulting normalized and modu-
lated GM and WM segments indicated the probability
(between 0 and 1) that a specific voxel belonged to the rel-
ative tissue. The modulated and normalized GM and WM
segments were written with VBM8 standard isotropic
voxel resolution of 1.5 mm3and smoothed with a 6 mm
FWHM Gaussian kernel, thus obeying the “rule of thumb”
that the FWHM should be at least twice the voxel dimen-
sion in order to ensure a Gaussian distribution of the
residuals of the General Linear Model (Moraschi et al.,
smoothed GM and WM images were used for analyses.
Statistical analyses of clinical variables were performed
with Statview Software.
Neuropsychological and psychopathological predictors
(considered as independent variables) of IS score (consid-
ered as the dependent variable) were assessed by using
stepwise multiple regression analyses, with a forward pro-
cedure and an F to enter of 4.
Preselection of independent variables to include in the
stepwise regression models was done by using correlation
analyses and Fisher’s r to z transformation, in order to
determine the significance of correlations. In the stepwise
multiple regression analysis only variables with P<0.05 in
the preselection correlation analyses were included as
To identify the brain regions in which patients showed
GM or WM volumetric correlates of the IS, two multiple
regression models (one for GM and one for WM) were
adopted using the IS as a regressor, and age and years of
formal education as covariates of no interest. Statistical anal-
yses were carried out at voxel level using SPM8. To avoid
type I errors (i.e., accepting false positives) all these analyses
were performed using the Random Fields Theory Family-
wise error (FWE) correction (P<0.05), which controls the
possibility of any false positives across the entire volume
(Ashburner and Friston, 2005). Further, results were consid-
ered statistically significant if they were part of a spatially
contiguous cluster size of 50 voxels or greater.
To obtain fine anatomical localization of statistical
results, two different brain atlases were used: (i) the auto-
mated anatomical labeling (Tzourio-Mazoyer et al., 2002),
which includes all main gyri and sulci of the cerebral cor-
tex and the subcortical and deep GM structures for a total
of 90 anatomical volumes of interest and (ii) the ICBMDTI-
81 WM labels atlas (Mori et al., 2005), which includes 50
WM tract labels created by manual segmentation of a
standard-space average of diffusion MRI tensor maps
from 81 subjects.
Mean GM and WM volumetric values of the global area
where significant relationship with IS scale were found,
were extracted for each subject as follows: statistical maps
of areas where significant relationship between IS scale
and GM (or WM) volumetric values emerged were first
saved as binary masks (i.e., maps where voxels have the
value of 1 where the relationship is significant and 0 other-
wise). Then, these masks were multiplied by GM (or WM)
maps in order to have, for each subject, a single map with
volumetric values only in areas where the relationship
was significant. Finally, using an in-house software written
in shell-script, we calculated the mean voxel value of these
areas, thus resulting, for each subject, in a mean GM (and
WM) value of voxels where the relationship between vol-
ume and IS score was significant. These two values (one
for GM and one for WM) were subsequently related to
clinical data by means of univariate correlation analyses.
The sociodemographic, psychopathological and neuro-
psychological characteristics of the patients are presented
in Table I.
rSpalletta et al. r
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Several areas of positive significant correlation between
GM volumes and IS scores were found, mostly in frontal
regions. Specifically, results were located in the pars orbi-
talis [Brodmann’s Area (BA) 11] and triangularis (BA 45)
of the left inferior frontal gyrus, in the right middle frontal
gyrus (BA 9), in bilateral precentral gyri (BA 6), in right
and left putamen, and in the right insula (BA 48).
Statistically significant positive correlations between
WM volumes and IS score emerged in bundles subserving
bilateral frontal regions, such as the bilateral cingulum, the
left anterior and superior corona radiata, the right superior
longitudinal fasciculus, and the left portion of the callosal
forceps minor (Table II; Figs. 1 and 2).
Univariate correlations between psychopathological and
neuropsychological variables and IS score are shown in
Table III, upper panel.
Preselection analyses revealed that IS score was signifi-
cantly and positively correlated with PANSS depression
score, and negatively correlated with RIR, CF, and DB
scores. The subsequent stepwise multiple regression analy-
sis showed that PANSS depression and DB scores were
significant predictors of IS index (Table III, lower panel).
The resulting equation was significant (F57.202; df52, 54;
P50.0017) and explained 21% of the overall variance of IS
score. In particular, higher PANSS depression score and
lower DB score predicted higher IS value.
Eventually, mean GM volumetric values of global areas
where significant relationships with IS scale were found,
negatively correlated with DB scores (r520.30, P50.021).
No significant relationships between mean WM volu-
metric values (in global areas where significant relation-
ships with IS scale were observed) and clinical scores were
The main aim of the present study was to investigate
the neuroanatomical correlates of metacognitive insight in
schizophrenia using GM and WM volumetric neuroimag-
ing techniques at the whole brain level. Secondary aims
were to investigate the psychopathological and neuropsy-
chological correlates of metacognitive insight, and the clin-
ical correlates of potential structural variations in brain
areas linked with metacognitive insight.
Four main results emerged. First, as hypothesized, a
reduced metacognitive insight, i.e., the ability to monitor
one’s own unusual changes in mental experiences as
expressed by lower IS scores, was related to reduced GM
volumes in the PFC. In particular, we found an association
in the left VLPFC and in the right DLPFC. In addition to
this, lower levels of metacognitive insight were related to
reduced GM volumes in other frontal cortical and deep
GM areas, as well as in the right insula. Second, as
expected, reduced metacognitive insight was related to
reduced WM volumes in several fascicules mainly con-
necting frontal areas, in particular right superior longitudi-
nal fasciculus, left forcep, minor and left corona radiata, as
well as bilateral cingulum. Third, higher ability to detect
changes in one’s mental activity, or increased metacogni-
tive insight, was related to higher depression severity and
reduced attentional control. Fourth, increased GM volumes
in those brain areas where a relationship with IS scores
emerged, were related to a reduced attention performance.
With regard to our first result, it is interesting to note
that all the brain areas highlighted by our study as related
to metacognitive insight are described in literature as
underpinning high-order cognitive functions, in particular
cognitive control and self-appraisal. Cognitive control is a
high-order function that encompasses the detection and
resolution of conflict among competing response alterna-
tives (Millerand Cohen,2001),thus suppressing
TABLE I. Sociodemographic, clinical, and neuropsycho-
logical characteristics of 57 patients with diagnosis of
Gender (male)42 (74)
Educational level (years)
Age at illness onset (years)
Illness duration (years)
Olanzapine equivalents (mg/day)
DB (correct answers)
MWCST achieved categories
MWCST perseverative errors
MWCST nonperseverative errors
PANSS Thought Disturbance
MMSE, Mini-Mental State Examination; RIR, Rey’s 15-word
Immediate Recall; RDR, Rey’s 15-word Delayed Recall; CD, Copy-
ing Drawings; CDL, Copying Drawings with Landmarks; PM47,
Raven’s Progressive Matrices’ 47; PVF, Phonological Verbal Flu-
ency; CF, Category Fluency Test; DB, Double Barrage; MWCST,
Modified Wisconsin Card Sorting Test; SD, Standard Deviation;
PANSS, Positive and Negative Syndrome Scale; GP, General psy-
chopathology; SARS, Simpson–Angus Rating Scale.
rNeural Correlates of Metacognitive Insight r
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Brain gray matter volumetric correlates of metacognitive insight
in 57 patients with diagnosis of schizophrenia. Areas of signifi-
cant relationship (P<0.05 FWE corrected) between gray matter
signal intensity and IS score. Scatterplot shows mean gray mat-
ter volumetric values plotted against individual IS scores. Regres-
sion line (dotted white) is also reported. Z coordinates are in
MNI space. IFG-PT: inferior frontal gyrus, pars triangularis; MFG:
middle frontal gyrus; OFC: orbitofrontal cortex; PG: precentral
TABLE II. Topography of the relationship between gray and white matter volumetry signal intensity and Insight
P (FWE-corr)Equiv Zx, y, z (mm)
Gray matter labels for peaks (BA)
28, 3, 16
28, 212, 12
42, 26, 40
44, 8, 54
39, 27, 46
215, 24, 224
214, 38, 218
212, 50, 221
227, 21, 13
239, 29, 39
46, 4, 7
242, 34, 19
Right precentral gyrus (6) 310
Right middle frontal gyrus (9)
Left inferior frontal gyrus, pars orbitalis (11)
Left precentral gyrus (6)
Right insula (48)
Left inferior frontal gyrus, pars triangularis (45)
White matter labels for peaks
Right superior longitudinal fasciculus
Left forceps minor
12, 26, 42
28, 212, 12
214, 42, 28
216, 33, 215
228, 8, 30
226, 17, 27
28, 10, 31
Left superior corona radiata
Left anterior corona radiata
FEW, family-wise error; BA, Brodman’s area.
Coordinates are in Montreal Neurological Institute Space.
interference or erroneous alternatives, and enhancing task
switching in order to guide behavior in accordance with
goals and contextual knowledge (Badre et al., 2009; Badre
and Wagner, 2006, 2007; Bunge et al., 2001). Left VLPFC
(pars orbitalis and triangularis of the left inferior frontal
gyrus) and right DLPFC (BA 9), are both significantly
involved in cognitive control, cognitive schemata regard-
ing one’s abilities, traits and attitudes that guide behaviors
and self-appraisal in social decision-making tasks (Johnson
et al., 2002; Schmitz and Johnson, 2006; Schmitz et al.,
2004). In particular, left pars triangularis, or mid-VLPFC,
appears to manage two specific forms of cognitive control,
i.e., active retrieval and proactive interference resolution
(Badre and Wagner, 2004; Kostopoulos and Petrides, 2008;
Petrides, 2005), that are strategic to isolate the target infor-
mation from other similar events (Kostopoulos et al., 2007;
Kostopoulos and Petrides, 2008) and to avoid that past
experiences interfere with processing a novel one (Badre
and Wagner, 2005, 2006). Further, left pars orbitalis (BA
11) is best involved in self-appraisal (Kjaer et al., 2002)
especially in situations characterized by uncertainty in the
course of evidence collection (Rushworth et al., 2007; Stern
et al., 2010). Differently, in a previous work on cognitive
insight (Orfei et al., 2013), right pars orbitalis was related
to the ability to question one’s judgments and beliefs and
to openness to corrective external feedback (Beck et al.,
2004). In the light of these findings, left and right pars
orbitalis seem to play different roles in cognitive control.
In fact, while right pars orbitalis elaborates alternative
hypotheses in cognitive tasks in which individuals are
required to generate a solution or response to problems,
when a variety of answers are possible (Trivedi et al.,
2008; Vartanian and Goel, 2005), left pars orbitalis best
deals with emotional reactions due to uncertainty in evalu-
ating self personality traits. This observation supports the
hypothesis that cognitive insight and metacognitive insight
catch two different, although related, aspects of self-
reflection that are modulated by either hemisphere. The
additional frontal cortical and subcortical areas related to
metacognitive insight are all highly structurally and func-
tionally connected to VLPFC and DLPFC such that they
seem to constitute a network playing a combined role in
emotional self-appraisal (Bergouignan et al., 2009; Ochsner
et al., 2005) and multisensory integration leading to self-
attribution (Ehrsson et al., 2004). Specifically, the putamen
is part and parcel of the frontostriatal neural network
managing response inhibition, and thus cognitive control
Brain white matter volumetric correlates of metacognitive
insight in 57 patients with diagnosis of schizophrenia. Areas of
significant relationship (P<0.05 FWE corrected) between white
matter signal intensity and IS score. Scatterplot shows mean
white matter volumetric values plotted against individual IS
scores. Regression line (dotted white) is also reported. Z coor-
dinates are in MNI space. A-SCR: anterior-superior corona radi-
ata; FM: forceps minor; SLF: superior longitudinal fasciculus.
TABLE III. Clinical and neuropsychological correlates of
Insight Scale score in 57 patients with diagnosis of
PANSS Positive Symptoms
PANSS Negative Symptoms
PANSS General Psychopathology
PANSS Thought disturbance
DB (correct answers)
MWCST achieved categories
MWCST perseverative errors
MWCST nonperseverative errors
PANSS, Positive and Negative Syndrome Scale; MMSE, Mini-
Mental State Examination; RIR, Rey’s 15-word Immediate Recall;
RDR, Rey’s 15-word Delayed Recall; CD, Copy Drawings; CDL,
Copy Drawings with Landmarks; PM 47, Raven’s Progressive
Matrices’ 47; PVF, Phonological Verbal Fluency; CF, Category Flu-
ency Test; DB, Double Barrage; MWCST, Modified Wisconsin
Card Sorting test.
Significant values at the uncorrected statistical level (P<0.05) are
highlighted in bold.
rNeural Correlates of Metacognitive Insight r
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(Ghahremani et al., 2012; van Schouwenburg et al., 2012).
Concurrently, functional MRI studies highlighted that
increasing activity in right anterior insula correlated with
interoceptive monitoring accuracy, and with measures of
subjective emotional experience (Critchley et al., 2004),
thus providing a substrate for subjective feeling states and
one’s sense of self (Critchley and Seth, 2012; Modinos
et al., 2009). Therefore, the cited prefrontal and frontal
areas are all involved in cognitive control and in self-
perception. In the light of these considerations, the rela-
tionship between prefrontal and frontal areas and meta-
cognitive insight is intriguing, since the latter may be
interpreted as the specific ability to monitor changes in the
self, requiring both an adequate self-perception and an
efficient cognitive control function, to permit the best pos-
sible selection among several hypotheses and judgments
about unusual mental events.
With regard to our second result, the relationship
between reduced WM volumes of a number of frontal fas-
cicules and reduced metacognitive insight can be traced
back to the structural and functional involvement in self-
perception of the cited bundles. In fact, the cingulum bun-
dles are the most prominent WM fiber tracts within the
limbic system, and connect the cingulate cortex with PFC,
premotor cortex, cortical association areas in the parietal
and occipital lobes, parahippocampal cortex and thalamus
(Abdul-Rahman et al., 2011). To note, lesions of cingulum
fasciculus showed to be involved in deficits in self-related
information processing (Sui et al., 2012). Further, WM
microstructural alterations in the superior longitudinal fas-
ciculus, the forceps minor and the corona radiata have
been frequently described in schizophrenia as related to
deficits in executive control functions (Clark et al., 2011;
Perez-Iglesias et al., 2010; Sasson et al., 2012).
Our third result, i.e., the depressive-attentional symp-
toms linked to higher metacognitive insight, deserves
some speculation. In fact, it is comprehensible that the
more the subject with schizophrenia diagnosis is able to
appreciate the strangeness of his/her own mental prod-
ucts, the more he/she is aware to be mentally ill. This
may, in turn, generate a depressive mood, as the stigma of
being mentally ill and the need for treatment or hospitali-
zation may heavily affect emotional well-being, quality of
life and even increasing risk of suicidality (Barrett et al.,
2010; Cooke et al., 2007; Crumlish et al., 2005; Gilbert
et al., 2000; Hasson-Ohayon et al., 2006). Further, it is well
known that higher depression severity entails cognitive
performances, specifically attentional control, memory
(Clark et al., 2009; Majer et al., 2004; McClintock et al.,
2010) and cognitive flexibility (Austin et al., 2001; Murphy
et al., 2012), as described also in schizophrenia (Iosifescu,
2012). We may speculate depression to play the role of
mediator between awareness of changes in the self and
cognitive performance. Conversely, it is also possible that
the observed negative correlation between quantitative
measures of cognitive functioning (i.e., number of DB cor-
rect responses) and metacognitive insight is accounted for
by the relative interdependence between meta-level control
processes and basic first-order cognitive processes respon-
sible for quantity performance (Koren et al., 2004; Koriat
and Goldsmith, 1996). Intriguingly, while increased meta-
cognitive insight was accompanied by an increase in vol-
ume in frontal cortical and subcortical areas, probably
resulting from adaptive plasticity to sustain adequate
impaired attentional control, as revealed by the reported
negative correlation between GM volume and correct
responses in a divided attention task. Increased cortical
volume/density in autism (Hazlett et al., 2011) reflects
subtle neuropathological changes involving neurons and
neuronal processes, such as a decrease in the normal
occurrence of dendritic pruning of superfluous neuronal
connections, resulting in increased dendritic arborization
(Hazlett et al., 2006). The same abnormalities in the neuro-
pil, expressed as an increase in neuronal density, have
been observed in chronic schizophrenic patients and sug-
gested to be more significant than neuronal loss in the
pathophysiology of the disorder (Glantz and Lewis, 2000;
Selemon et al., 1998). We can therefore speculate that the
association between increased prefrontal cortical and sub-
cortical GM volume and attentional control impairment
observed in the present sample may be the consequence of
subtle abnormalities involving neurones or neuronal proc-
esses in the frontal and subcortical cortices which may dis-
rupt theneural circuitry
information processing required for attentional control.
However, our speculation on the causal chain between
these factors deserves further deepening.
Before the conclusions some issues limiting the general-
izability of our results have to be acknowledged. First,
patients were recruited at various lengths of illness and
we may wonder whether the same structure–function rela-
tionship is observable in a more homogeneous sample at
the onset of the illness, such as in first-episode patients.
Thus, further research on this point is needed. Second, as
our sample was homogeneous with regard to race and
recruitment source, it is not clear whether the same results
would emerge from other populations. Thus, future
research might replicate the study on different samples.
Third, the patients in our sample were in a stable phase of
illness and were being treated with stable doses of anti-
psychotics; therefore, we cannot exclude that medication
affected metacognitive insight. Fourth, we included only
nondemented patients with a MMSE score equal to or
higher than 24 to avoid a confounding effect on the data,
because they potentially could not fully understand the IS
items. Therefore, it must be determined whether this selec-
tion criterion created a bias. Fifth, the cross-sectional struc-
ture of the study might also be a limitation. In fact, a
longitudinal study on trajectories of metacognitive insight
might have been more informative in predicting patients’
outcomes, including treatment response and relationships
with depressive andcognitive
sectional designs are confounded by age cohort effects
in the sameareas
symptoms, as cross-
rSpalletta et al. r
r 8 r
(Thompson et al., 2011). Sixth, the IS has been developed
to elicitate subject’s perception of even subtle change in
the self, it appears best suitable in the very early stages of
illness, for instance in first-episode patients. Indeed, in our
study the sample described is quite heterogeneous with
regard to illness duration, thus encompassing also chronic
patients. This might represent a confounding factor. How-
ever, this issue is rather an interpretation, since the origi-
nal population of reference for the development of the
questionnaire was not specified by Markov? a and Berrios in
terms of illness duration. Thus, future studies investigating
required to clarify this point. Finally, the neural regions
found to be significantly correlated with IS scores are pre-
dominantly prefrontal and underpin executive functions.
However, contrary to what is described with regard to
clinical (Aleman et al., 2006) and cognitive insight (Orfei
et al., 2010), we have found a correlation between IS and
DB, but not MWCST indexes. Indeed, the term “executive
functions” is quite wide and encompasses various specific
functions. The MWCST is thought to investigate "set-shift-
ing," i.e., the ability to display flexibility in the face of
changing schedules of reinforcement. On the other hand,
the DB test is aimed at investigating attentional processes.
Thus, it is possible that clinical and cognitive insight
dimensions are more dependent on strategic planning,
organized searching, utilizing environmental feedback to
shift cognitive sets, directing behavior toward achieving a
goal, and modulating impulsive responding, while the
metacognitive dimension of insight is better dependent on
attentional processes. These data would support the spe-
cific nature of metacognitive insight with respect other
In conclusion, this study explored for the first time the
neuroanatomy of what we named metacognitive insight,
that is a form of self-monitoring of changes in subjective
self-experience and the ability to feel and express the
related emotional unease in patients affected by schizo-
phrenia (Markov? a et al., 2003) and its relationships with
psychopathological and neuropsychological characteristics.
The data emerging from our study confirm our hypotheses
that metacognitive insight is mediated by a frontostriatal-
limbic circuitry, which would involve some specific corti-
cal and subcortical GM areas and WM bundles. In
particular, metacognitive insight appears to be under-
pinned by top-down cognitive control processes, which
would allow the selection among a number of alternative
self-representations retrieved from semantic memory and
based on a flexible updating of information about the self.
This ability in detecting and integrating new elements and
cues, would indicate that metacognitive insight reflects
patient’s actual self-beliefs.
Thus, while clinical insight mostly focuses on patients’
verbal statements regarding their mental illness and need
for treatment, metacognitive insight is centered on the abil-
ity to detect changes in sensations and a related sense of
strangeness, which hardly can be described when not
actually experienced. Also,
explores general cognitive styles that can deal with various
issues, even in healthy subjects, metacognitive insight spe-
cifically investigates metacognitive abilities applied to
awareness of illness in patients with psychosis. Further,
the IS, being focused on awareness of changes in self-
perception, may be best informative in first-episode sub-
jects and in the onset stages of illness while the classical
scales for clinical insight best investigate more stable path-
The study of metacognitive insight may provide a
deeper knowledge about the processes underpinning
awareness deficits and, more in general, self-monitoring
and self-knowledge. Moreover, since metacognitive insight
focuses on patient’s ability to reflect on oneself and on the
update, it may provide also relevant indications for the
development of specific cognitive therapeutic strategies,
aimed at improving self-monitoring and flexibility of
thought applied to the detection and evaluation of changes
in the self. In turn, this is supposed to encourage compli-
ance to treatment and thus an improvement in quality of
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