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. 2023 May 8;3(2):100173. doi: 10.1016/j.ynirp.2023.100173

Perceiving visual negative stimuli in schizophrenia and bipolar disorder: Meta-analytic evidence of a common altered thalamic-parahippocampal-basal ganglia circuit

Alessandro Grecucci a,b,∗∗,1, Chiara Orsini c,∗,1, Gaia Lapomarda a,d, Sara Sorella a, Irene Messina a,e
PMCID: PMC12172923  PMID: 40568452

Abstract

Despite the kraepelinian differentiation of schizophrenia and bipolar disorder, several data questioned this net subdivision and suggested a continuity between the two. An expanded continuum hypothesis was suggested, assuming a common psychotic core between the two disorders, as well as cognitive and affective differences. The present study aimed to investigate similarities and differences between schizophrenia and bipolar disorder for what entails the affective dimension of the continuum. A coordinate-based meta-analytic approach on neuroimaging data was applied to understand differences and similarities in the visual perception of negative stimuli in the two groups. The activation likelihood estimation analysis included 41 experiments on schizophrenia (schizophrenia versus healthy controls) and 27 experiments on bipolar disorder (bipolar versus healthy controls). Our conjunction analysis results revealed the presence of shared functional abnormalities in thalamic, parahippocampal, and basal ganglia areas, suggesting that these patients share an altered circuit responsible for a heightened elaboration of negative emotional stimuli. The subtraction analysis highlighted that the two groups present differences too. Schizophrenia patients show widespread abnormalities in limbic, temporal, sub-lobar and midbrain regions possibly involved in emotional processing and hallucinations. On the other hand, bipolar patients show alterations in frontal areas associated with emotional appraisal, regulation, and response inhibition. This study sheds light on both similarities and differences in the emotional processing of schizophrenic and bipolar patients, and may help to better characterise the affective features of these two conditions along a continuum.

Keywords: Schizophrenia, Bipolar, Emotion, Meta-analysis, Perception

Graphical abstract

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Highlights

  • •

    This study tested the SCZ-BD continuum hypothesis during negative emotion processing.

  • •

    Patients share hyperactivation in thalamus, basal ganglia and parahippocampal gyrus.

  • •

    SCZ differs from BD in limbic, temporal, claustral, midbrain areas.

  • •

    BD differs from SCZ in IFG, MFG and precentral gyrus.

1. Introduction

Schizophrenia (SCZ) and bipolar disorder (BD) are two severe psychiatric illnesses whose diagnostic history intertwined over the course of time (see Angst, 2002 for a review). Despite they are still considered as separate disorders (DSM-V, American Psychiatric Association [APA], 2013; ICD-10, World Health Organization [WHO], 1993), similarities in the phenomenology of some symptoms (e.g., hallucinations, delusions), and increasing evidence coming from genetic, behavioural and neuroimaging research led several authors to consider these two conditions as part of a continuum (Crow, 1986; Sorella et al., 2019; for a review see Möller, 2003). There is evidence that first-degree relatives of either SCZ or BD patients are at increased risk to develop both disorders (Lichtenstein et al., 2009; Van Snellenberg and de Candia, 2009). In addition, genetic overlap between these disorders has been observed (Ahmadi et al., 2018; Badner and Gershon, 2002; Cheon et al., 2022; Vazza et al., 2007; for a review see Craddock et al., 2005), and, consistently, behavioural studies reported shared impairments in executive functions (Ancín et al., 2013; Kravariti et al., 2005), memory (Au et al., 2017; Pradhan et al., 2008), verbal fluency, motor speed (Caletti et al., 2013; Kuswanto et al., 2013), and emotion recognition (Goghari and Sponheim, 2013).

Brain imaging research provided relevant insights as well, looking for shared structural brain abnormalities between SCZ and BD (Cheon et al., 2022; Ellison-Wright and Bullmore, 2010; Hulshoff Pol et al., 2012; Knöchel et al., 2016; Yu et al., 2010; Zhao et al., 2021). Notably, Sorella et al. (2019) applied an unsupervised machine learning approach to investigate the overlap between SCZ and BD, by taking into account morphometric features as well as cognitive, affective, and symptomatology measures, and suggested the hypothesis of an expanded continuum (Sorella et al., 2019). According to this hypothesis, the similarities between SCZ and BD patients include different alterations in three cores: (a) the psychotic core, which involves portions of the medial parietal and temporo-occipital areas, parts of the cerebellum and the middle frontal gyrus; (b) the cognitive core, that is more compromised in SCZ rather than in BD and includes alterations in executive fronto-parietal areas; and, (c) the affective core, more altered in BD compared to SCZ, which includes portions of the temporal and occipital lobes, cerebellum, and frontal gyrus. These results are in line with those showing that cognitive impairments in BD patients are milder than the ones of SCZ patients (Bora and Pantelis, 2015; Krabbendam et al., 2005; Kuswanto et al., 2016; Sheffield et al., 2018), whereas the differences in the affective core are currently less understood. In the present study, we aimed to better characterise the affective dimension of the expanded continuum hypothesis, capitalising on a meta-analytic approach of brain functional studies on emotional processing in SCZ and BD patients.

It is well known that negative emotions can trigger hallucinations (Laloyaux et al., 2019; Waters et al., 2012) and delusions (for reviews see Freeman, 2007; Freeman and Garety, 2003), in both disorders. Importantly, emotions can also contribute to the maintenance of both delusions and hallucinations (Freeman and Garety, 2003). Emotion regulation deficits and emotional lability depending on fronto-limbic alterations are largely documented in BD (see the review by Townsend and Altshuler, 2012), while SCZ patients present dysfunctions in the domains of experience, perception, recognition, and expression of emotions (see Aleman and Kahn, 2005; Kohler and Martin, 2006; Trémeau, 2006 for reviews) and abnormalities in emotional memory (Herbener, 2008), in particular the intrusive and the negative ones – that can influence the content and meaning of hallucinations (Laloyaux et al., 2019; Waters et al., 2012). Previous meta-analyses on emotional processing in SCZ and BD separately showed BOLD signal alterations in limbic regions associated with emotional processing, such as the amygdala, anterior cingulated cortex, thalamic, occipital, frontal, and temporal areas (Chen et al., 2011; Dong et al., 2018; Houenou et al., 2011; H. J. Li et al., 2010; Taylor et al., 2012; Wegbreit et al., 2014). Another meta-analysis directly compared SCZ and BD patients, focusing on the processing of facial expressions (Delvecchio et al., 2013). Results showed different activity at the level of thalamus and cuneus, but any similarity emerged (Delvecchio et al., 2013). Taking into account the literature reviewed, no previous meta-analyses investigated shared and distinct brain functional mechanisms underlying the perception of visual negative stimuli (not limited to facial expressions) in both SCZ and BD. Thus, in the present study, we applied the activation likelihood estimation (ALE) meta-analytical technique on studies exploring the visual perception of a broad range of negative stimuli in SCZ and BD patients. The meta-analytic approach is a useful method to generalise the results of different studies on the same research topic (Gurevitch et al., 2018), reducing inconsistencies and discrepancies between them (Stone and Rosopa, 2017), and overcoming sample-size limitations (Berman and Parker, 2002). Furthermore, by computing the conjunction between two classes of patients, we can shed light on possible overlapping mechanisms and commonalities between disorders.

In light of what has emerged so far, we expect to find shared altered activity in regions underlying the abnormal affective experience both disorders suffer from (Chen et al., 2011; Houenou et al., 2011; Lapomarda et al., 2021a, 2021b; Dong et al., 2018; Taylor et al., 2012). Specifically, we hypothesise common alterations in a circuit including the thalamus and the basal ganglia, known to be involved in exaggerated affective experiences (e.g. emotions, impulses, mood, salience attribution) that characterise both disorders (Delvecchio et al., 2012; Howes and Kapur, 2009; Lapomarda et al., 2021a, 2021b; Sorella et al., 2019). We also expect common parahippocampal alterations due to its role in the elaboration of negative stimuli (for a review see Lew and Semendeferi, 2017) and in psychosis (Bodnar et al., 2011; Diederen et al., 2010; T. Yu et al., 2020). Furthermore, we predict abnormal activation in frontal areas involved in emotion control and regulation in BD (see Hou et al., 2017; and Townsend and Altshuler, 2012 for reviews, and Sorella et al., 2019), and in medial-temporal regions such as amygdala and hippocampus in SCZ for their role in emotional processes and dysfunctions associated with the pathology (Aleman and Kahn, 2005; Bolton et al., 2012; Bowen et al., 2018; Ganzola et al., 2014).

2. Materials and methods

2.1. Search criteria and study selection

Studies were collected through two systematic reviews (one for each clinical group) conducted in July 2020 from three online databases: PubMed (https://www.ncbi.nlm.nih.gov/pubmed/), Web of Science (http://www.webofknowledge.com/) and ScienceDirect (https://www.sciencedirect.com). The research queries for the schizophrenia group were schizophrenia AND (fMRI OR “functional magnetic resonance”) AND emotion AND (processing OR perception) AND (face OR faces OR facial) for facial stimuli and schizophrenia AND (fMRI OR “functional magnetic resonance”) AND (IAPS OR “international affective picture system”) for the IAPS ones. Similarly, the search terms for the bipolar group were bipolar AND (fMRI OR “functional magnetic resonance”) AND emotion AND (processing OR perception) AND (face OR faces OR facial) for facial stimuli and bipolar AND (fMRI OR “functional magnetic resonance”) AND (IAPS OR “international affective picture system”) for IAPS stimuli.

This research yielded a total of 4238 results for schizophrenia and 2961 results for bipolar disorder, which have been consequently selected according to the following selection criteria.

  • a)

    Studies published in English.

  • b)

    Patients specifically diagnosed with schizophrenia (schizophrenia spectrum disorders were excluded) or bipolar disorder (Type I and II) and control group composed of healthy participants.

  • c)

    Participants entered at least in their eighteenth year of age (17 years old) considering the standard deviation range reported in papers' demographical section.

  • d)

    Use of fMRI in order to evaluate differences in emotion perception activation between pathological and healthy groups.

  • e)

    The task used negative IAPS (Lang et al., 2008) or facial emotion (angry, sad, fearful, disgusted) stimuli and contrasted them, respectively, with neutral IAPS or neutral faces in the same experimental condition (e.g. 2-back negative vs. 1-back neutral experiments were excluded, while the 2-back negative vs. 2-back neutral ones were included).

  • f)

    fMRI ROIs or whole brain coordinates reported and provided in MNI or Talairach stereotactic spaces. When studies presented both ROIs and whole brain data, all of them were included.

Through this study selection, 20 studies for the schizophrenia group and 17 for the bipolar group were identified. Aggregated results were used if available. When not aggregated (e.g. there were only separate analyses and results for different patients subgroups, different emotions, tasks, or different emotional stimuli intensity/arousal/consciousness), these data were considered as separated results. These experiments were then inserted separately in our datasets and analyses. In line with that, 41 experiments for the schizophrenia group and 27 experiments for the bipolar disorder group were identified (Table 1 and Supplementary Table S1). Participants were 2548, specifically 840 SCZ (mean age = 31.08 ± 8.51), 392 BD (mean age = 36.30 ± 10.83) and 1316 HC (mean age = 31.78 ± 9.27).

Table 1.

Main details and description of the studies included in the meta-analysis.

N° paper Studies N Age Stimuli Task Contrast WB/ROIs Foci hypoactivations Foci hyperactivations
SCHIZOPHRENIA
1 Hall et al. (2008) 19 (SCZ)24 (HC) 37.7 ± 8.4 (SCZ)35.1 ± 9.7 (HC) FACES Gender ID FEAR vs NEUTRAL WB + ROIs 3 –
2 H. J. Li et al. (2012) 12 (SCZ)12 (HC) 29.8 ± 9.24 (SCZ)29.25 ± 7.24 (HC) FACES Judge valence FEAR vs NEUTRAL WB + ROIs 1 1
3 Michalopoulou et al. (2008) 11 (SCZ)9 (HC) 35 ± 9 (SCZ)32 ± 6 (HC) FACES Gender ID FEAR vs NEUTRAL WB 6 –
4 Mier et al. (2014) 11 (SCZ)16 (HC) 32.45 ± 7.66 (SCZ)34.5 ± 6.47 (HC) FACES Emotion ID FEAR vs NEUTRAL ROIs 2 –
5 11 (SCZ)16 (HC) 32.45 ± 7.66 (SCZ)34.5 ± 6.47 (HC) FACES Emotion ID ANGER vs NEUTRAL ROIs 1 –
6 11 (SCZ)16 (HC) 32.45 ± 7.66 (SCZ)34.5 ± 6.47 (HC) FACES Emotion ID DISGUST vs NEUTRAL ROIs – –
7 Williams et al. (2004) 27 (SCZ paranoid + non-paranoid)22 (HC) 27.3 ± 9.6 (SCZ paranoid + non-paranoid)27.2 ± 8.1 (HC) FACES Gender ID FEAR vs NEUTRAL WB 5 –
8 Williams et al. (2007) 13 (SCZ paranoid)13 (HC) 26.9 ± 9.1 (SCZ paranoid)25.1 ± 8.1 (HC) FACES Gender ID With arousal FEAR vs NEUTRAL WB + ROIs 2 –
9 14 (SCZ non-paranoid)13 (HC) 27.8 ± 10.4 (SCZ non-paranoid)25.1 ± 8.1 (HC) FACES Gender ID With arousal FEAR vs NEUTRAL WB + ROIs 2 –
10 13 (SCZ paranoid)13 (HC) 26.9 ± 9.1 (SCZ paranoid)25.1 ± 8.1 (HC) FACES Gender ID Without arousal FEAR vs NEUTRAL WB + ROIs 1 –
11 14 (SCZ non-paranoid)13 (HC) 27.8 ± 10.4 (SCZ non-paranoid)25.1 ± 8.1 (HC) FACES Gender ID Without arousal FEAR vs NEUTRAL WB + ROIs 1 –
12 13 (SCZ paranoid)13 (HC) 26.9 ± 9.1 (SCZ paranoid)25.1 ± 8.1 (HC) FACES Gender ID With arousal ANGER vs NEUTRAL WB + ROIs 1 –
13 14 (SCZ non-paranoid)13 (HC) 27.8 ± 10.4 (SCZ non-paranoid)25.1 ± 8.1 (HC) FACES Gender ID With arousal ANGER vs NEUTRAL WB + ROIs – –
14 13 (SCZ paranoid)13 (HC) 26.9 ± 9.1 (SCZ paranoid)25.1 ± 8.1 (HC) FACES Gender ID Without arousal ANGER vs NEUTRAL WB + ROIs 2 –
15 14 (SCZ non-paranoid)13 (HC) 27.8 ± 10.4 (SCZ non-paranoid)25.1 ± 8.1 (HC) FACES Gender ID Without arousal ANGER vs NEUTRAL WB + ROIs 2 –
16 13 (SCZ paranoid)13 (HC) 26.9 ± 9.1 (SCZ paranoid)25.1 ± 8.1 (HC) FACES Gender ID With arousal DISGUST vs NEUTRAL WB + ROIs 1 –
17 14 (SCZ non-paranoid)13 (HC) 27.8 ± 10.4 (SCZ non-paranoid)25.1 ± 8.1 (HC) FACES Gender ID With arousal DISGUST vs NEUTRAL WB + ROIs 1 –
18 13 (SCZ paranoid)13 (HC) 26.9 ± 9.1 (SCZ paranoid)25.1 ± 8.1 (HC) FACES Gender ID Without arousal DISGUST vs NEUTRAL WB + ROIs – –
19 14 (SCZ non-paranoid)13 (HC) 27.8 ± 10.4 (SCZ non-paranoid)25.1 ± 8.1 (HC) FACES Gender ID Without arousal DISGUST vs NEUTRAL WB + ROIs 1 –
20 Surguladze et al. (2011) 16 (SCZ conventional antipsychotic)16 (HC) 43.7 ± 9.4 (SCZ conventional antipsychotic)40.4 ± 12.8 (HC) FACES Gender ID FEAR vs NEUTRAL WB + ROIs 1 1
21 16 (SCZ risperidone)16 (HC) 42.6 ± 11.7 (SCZ risperidone)40.4 ± 12.8 (HC) FACES Gender ID FEAR vs NEUTRAL WB + ROIs – –
22 Lindner et al. (2014) 36 (SCZ)40 (HC) 30.8 ± 7.9 (SCZ)29.5 ± 8.3 (HC) FACES Passive view Masked DISGUST vs NEUTRAL WB + ROIs 4 2
23 36 (SCZ)40 (HC) 30.8 ± 7.9 (SCZ)29.5 ± 8.3 (HC) FACES Passive view Unmasked DISGUST vs NEUTRAL WB + ROIs 1 8
24 Rauch et al. (2010) 12 (SCZ)12 (HC) 27.7 ± 7.5 (SCZ)26.9 ± 6.1 (HC) FACES Rate valence (masked faces) Masked SAD vs NEUTRAL ROIs – 3
25 Lindner et al. (2016) 36 (SCZ with flat affect + without)40 (HC) 30.6 ± 8 (SCZ with flat affect + without)29.5 ± 8.3 (HC) FACES View and memorise Masked FEAR vs NEUTRAL WB + ROIs – –
26 36 (SCZ with flat affect + without)40 (HC) 30.6 ± 8 (SCZ with flat affect + without)29.5 ± 8.3 (HC) FACES View and memorise Masked DISGUST vs NEUTRAL ROIs – –
27 34 (SCZ with flat affect + without)40 (HC) 30.6 ± 8 (SCZ with flat affect + without)29.5 ± 8.3 (HC) FACES View and memorise Unmasked FEAR vs NEUTRAL WB + ROIs – 5
28 34 (SCZ with flat affect + without)40 (HC) 30.6 ± 8 (SCZ with flat affect + without)29.5 ± 8.3 (HC) FACES View and memorise Unmasked DISGUST vs NEUTRAL ROIs – 2
29 Das et al. (2007) 14 (first episode SCZ)14 (HC) 20.4 ± 3.3 (first episode SCZ)23.1 ± 5.9 (HC) FACES Emotion ID Unmasked FEAR vs NEUTRAL ROIs 6 5
30 13 (first episode SCZ)14 (HC) 20.4 ± 3.3 (first episode SCZ)23.1 ± 5.9 (HC) FACES Emotion ID Masked FEAR vs NEUTRAL ROIs 5 3
31 Becerril and Barch (2011) 38 (SCZ)32 (HC) 36.66 ± 9.12 (SCZ)36.19 ± 10.86 (HC) FACES 2-back working memory FEAR vs NEUTRAL WB + ROIs – 18
32 Reske et al. (2009) 18 (first episode SCZ)18 (HC) 31.94 ± 6.41 (first episode SCZ)31.94 ± 6.03 (HC) FACES Emotion ID SAD vs NEUTRAL WB – 1
33 Takahashi et al. (2004) 15 (SCZ)15 (HC) 29.0 ± 6.9 (SCZ)29.1 ± 7.8 (HC) IAPS Categorise the emotion felt NEGATIVE vs NEUTRAL WB 10 –
34 Mendrek et al. (2012) 17 (SCZ follicular phase)15 (HC) 32.86 ± 6.56 (SCZ follicular phase)29.28 ± 9.27 (HC) IAPS Passive view + indicate images with a person/part of it NEGATIVE vs NEUTRAL WB + ROIs – –
35 17 (SCZ luteal phase)15 (HC) 32.86 ± 6.56 (SCZ luteal phase)29.28 ± 9.27 (HC) IAPS Passive view + indicate images with a person/part of it NEGATIVE vs NEUTRAL WB + ROIs 15 1
36 Champagne et al. (2012) 43 (SCZ female + male)43 (HC) 32.86 ± 6.56 (SCZ female)31.36 ± 7.36 (SCZ male)29.28 ± 9.27 (HC female)30.55 ± 7.81 (HC male) IAPS Passive view NEGATIVE vs NEUTRAL WB – –
37 Hägele et al. (2016) 37 (SCZ)40 (HC) 31 ± 9.4 (SCZ)36.3 ± 11.8 (HC) IAPS Passive view + confirm picture view NEGATIVE vs NEUTRAL WB + ROIs – –
38 Lakis et al. (2011) 37 (SCZ)37 (HC) 32.46 ± 7.66 (SCZ)31.81 ± 6.91 (HC) IAPS Episodic memory retrieval (yes/no recognition paradigms) Low arousal NEGATIVE vs NEUTRAL WB 3 –
39 37 (SCZ)37 (HC) 32.46 ± 7.66 (SCZ)31.81 ± 6.91 (HC) IAPS Episodic memory retrieval (yes/no recognition paradigms) High arousal NEGATIVE vs NEUTRAL WB 2 –
40 Anticevic et al. (2011) 23 (SCZ)23 (HC) 36.39 ± 9.54 (SCZ)37.18 ± 7.59 (HC) IAPS Delayed match-to-sample visual working memory (IAPS = distractors) NEGATIVE vs NEUTRAL WB + ROIs 2 –
41 Diaz et al. (2011) 11 (SCZ)17 (HC) 32.57 ± 12.7 (SCZ)24.01 ± 3.89 (HC) IAPS View IAPS during maintaining memoranda in verbal working memory task (IAPS = distractors) NEGATIVE vs NEUTRAL WB + ROIs 5 –
BIPOLAR DISORDER
1 Lawrence et al. (2004) 12 (BD-I euthymic)11 (HC) 41 ± 11 (BD-I euthymic)41 ± 11 (HC) FACES Gender ID 50% FEAR vs NEUTRAL ROIs 2 1
2 12 (BD-I euthymic)11 (HC) 41 ± 11 (BD-I euthymic)41 ± 11 (HC) FACES Gender ID 100% FEAR vs NEUTRAL ROIs – 1
3 12 (BD-I euthymic)11 (HC) 41 ± 11 (BD-I euthymic)41 ± 11 (HC) FACES Gender ID 50% SAD vs NEUTRAL ROIs 3 2
4 12 (BD-I euthymic)11 (HC) 41 ± 11 (BD-I euthymic)41 ± 11 (HC) FACES Gender ID 100% SAD vs NEUTRAL ROIs 2 2
5 Lennox et al. (2004) 10 (BD-I manic)12 (HC) 37.3 ± 12.8 (BD-I manic)32.6 ± 10.7 (HC) FACES Rate Emotion Intensity SAD vs NEUTRAL WB + ROIs 7 5
6 Malhi et al. (2007) 10 (BD-I euthymic)10 (HC) 33.5 ± 8.7 (BD-I euthymic)32.4 ± 6.4 (HC) FACES Emotion ID FEAR vs NEUTRAL WB 1 9
7 10 (BD-I euthymic)10 (HC) 33.5 ± 8.7 (BD-I euthymic)32.4 ± 6.4 (HC) FACES Emotion ID DISGUST vs NEUTRAL WB 15 4
8 Jogia et al. (2008) 8 (BD-I baseline)12 (HC) 42.1 ± 11.8 (BD-I baseline)41.8 ± 10.9 (HC) FACES Emotion ID SAD vs NEUTRAL WB 5 1
9 Sagar et al. (2013) 23 (BD-I euthymic)18 (HC) 26.65 ± 6.65 (BD-I euthymic)23.11 ± 3.15 (HC) FACES Backward-masked affect paradigm (Gender ID) Masked FEAR vs NEUTRAL ROIs 4 22
10 Marchand et al. (2011) 16 (BD-II depressed)19 (HC) 32.9 ± 7.5 (BD-II depressed)33.7 ± 12.5 (HC) FACES Match Identity AND Expression FEAR vs NEUTRAL WB + ROIs – –
11 Surguladze et al. (2010) 20 (BD-I euthymic)20 (HC) 42.7 ± 10.4 (BD-I euthymic)41.9 ± 11.6 (HC) FACES Gender ID FEAR vs NEUTRAL WB + ROIs – 2
12 Mullin et al. (2012) 22 (BD-I euthymic)19 (HC) 31.68 ± 8.96 (BD-I euthymic)32.54 ± 6.56 (HC) FACES 2-back working memory FEAR vs NEUTRAL ROIs – 5
13 Chen et al. (2006) 8 (BD-I manic)8 (HC) 39 ± 13.44 (BD-I manic)38.75 ± 12.5 (HC) FACES Rate emotion intensity + Rate colour intensity FEAR vs NEUTRAL WB – 8
14 8 (BD-I manic)8 (HC) 39 ± 13.44 (BD-I manic)38.75 ± 12.5 (HC) FACES Rate emotion intensity + Rate colour intensity SAD vs NEUTRAL WB – 1
15 8 (BD-I depressed)8 (HC) 41.88 ± 12.09 (BD-I depressed)38.75 ± 12.5 (HC) FACES Rate emotion intensity + Rate colour intensity FEAR vs NEUTRAL WB – 8
16 8 (BD-I depressed)8 (HC) 41.88 ± 12.09 (BD-I depressed)38.75 ± 12.5 (HC) FACES Rate emotion intensity + Rate colour intensity SAD vs NEUTRAL WB – –
17 Grotegerd et al. (2014) 22 (BD-I depressed)22 (HC) 42 ± 11 (BD-I depressed)41.1 ± 10.9 (HC) FACES Rate masked valence Masked SAD vs NEUTRAL ROIs 1 –
18 Deveney et al. (2014) 22 (BD-I/II adults, euthymic or depressed)19 (HC adults) 35.54 ± 11.2 (BD-I/II adults, euthymic or depressed)32.8 ± 11.4 (HC adults) FACES Rate hostility (explicit) ANGER vs NEUTRAL WB + ROIs 1 –
19 22 (BD-I/II adults, euthymic or depressed)19 (HC adults) 35.54 ± 11.2 (BD-I/II adults, euthymic or depressed)32.8 ± 11.4 (HC adults) FACES Rate nose width (implicit) ANGER vs NEUTRAL WB + ROIs – –
20 Rootes-Murdy et al. (2019) 8 (BD-I lithium responders, euthymic)21 (HC) 41.63 ± 14.03 (BD-I lithium responders, euthymic)36.33 ± 12.96 (HC) FACES Emotion induction (view) FEAR vs NEUTRAL WB 1 –
21 4 (BD-I lithium non-responders, euthymic)21 (HC) 29.75 ± 12.18 (BD-I lithium non-responders, euthymic)36.33 ± 12.96 (HC) FACES Emotion induction (view) FEAR vs NEUTRAL WB – –
22 Bermpohl et al. (2009) 10 (BD-I manic)10 (HC) 37.9 ± 13.2 (BD-I manic)35.8 ± 12.9 (HC) IAPS Passive view + confirm picture view NEGATIVE vs NEUTRAL ROIs – –
23 Cerullo et al. (2014) 25 (BD-I depressed)25 (HC) 30 ± 8 (BD-I depressed)26 ± 7 (HC) IAPS Continuous performance task with emotional and neutral distracters (CPT-END) NEGATIVE vs NEUTRAL WB 5 11
24 L. Li et al. (2019) 13 (BD-I lithium, euthymic)16 (HC) 29.6 ± 9.12 (BD-I lithium, euthymic)29.4 ± 7.47 (HC) IAPS Rate valence NEGATIVE vs NEUTRAL WB + ROIs – –
25 16 (BD-I valproate, euthymic)16 (HC) 32.8 ± 8.18 (BD-I valproate, euthymic)29.4 ± 7.47 (HC) IAPS Rate valence NEGATIVE vs NEUTRAL WB + ROIs – –
26 Hägele et al. (2016) 12 (BD-I manic)40 (HC) 40.7 ± 14.4 (BD-I manic)36.3 ± 11.8 (HC) IAPS Passive view + confirm picture view NEGATIVE vs NEUTRAL WB + ROIs – –
27 Ellard et al. (2019) 39 (BD-I depressed)36 (HC) 36.69 ± 12.92 (BD-I depressed)34.69 ± 12.64 (HC) IAPS Multi-Source Interference Task (MSIT) NEGATIVE vs NEUTRAL ROIs – –
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2.2. Activation likelihood estimation procedure

This neuroimaging coordinate-based meta-analysis was performed using the activation likelihood estimation (ALE) procedure (Eickhoff et al., 2009, 2012; Turkeltaub et al., 2012), through the GingerALE software v3.0.2 (http://brainmap.org/). The ALE algorithm estimates the significant convergence between neuroimaging experiments (Eickhoff et al., 2012). It models different studies’ foci as three-dimensional Gaussian probability distributions centered at the foci coordinates, therefore considering the spatial uncertainty associated to the determination of their xyz locations (Eickhoff et al., 2009; Turkeltaub et al., 2002). Then the maximum probability relative to each Gaussian-modeled foci belonging to the same experiment is calculated, obtaining Modeled Activation (MA) maps: the voxelwise union of the latter provides the ALE map (Turkeltaub et al., 2012). A permutation test discriminates the effective significant convergence from the noise (Eickhoff et al., 2009, 2012).

In the present study, the selected foci reported in Talairach space (Talairach and Tournoux, 1988) have been converted in the Montreal Neurological Institute (MNI) one (Collins et al., 1994; Evans et al., 1993) through the GingerALE “Convert Foci” tool, which performs the transformation using the icbm2tal algorithm developed by Lancaster et al., in 2007. Again, when the selected studies applied the mni2tal transformation (Brett et al., 2001, 2002), its effect has been removed using this tool in order to increase the precision of our study (Laird et al., 2010). After this spatial conversion, two separate datasets (one for the hyperactivation and one for the hypoactivation) for each clinical group were created, generating a total of four files (SCZ > HC = 41 experiments, 50 foci, 1715 participants; SCZ < HC = 41 experiments, 86 foci, 1715 participants; BD > HC = 27 experiments, 82 foci, 833 participants; BD < HC = 27 experiments, 47 foci, 833 participants). Before starting the analyses, GingerALE preference “Cluster Analysis Labels” was set on “Gray Matter Only”. Then, the previously mentioned files were separately entered in GingerALE as inputs for single datasets analyses (first-level analyses), applying a cluster-level family-wise error (FWE) < 0.05, threshold permutations equal to 2000 and p-value <0.05.

Since the aim of this study was to directly compare brain activation abnormalities between SCZ and BD by contrast analyses, we launched the same procedure also for two BD and SCZ pooled datasets: one for patients hypoactivation (POOLED < HC = 68 experiments, 133 foci, 2548 participants) and one for hyperactivation (POOLED > HC = 68 experiments, 132 foci, 2548 participants). This first step provided above threshold ALE maps representing separately the hypoactivation and hyperactivation of bipolar disorder, schizophrenia, and pooled patients group with respect to healthy controls. Furthermore, in order to verify whether altered functional substrates presenting a difference in the direction of abnormal activation exist between the two clinical groups, two additional pooled datasets were created. Specifically, the BD > HC dataset was pooled with the SCZ < HC one (leading to a pooled file containing 68 experiments, 168 foci, and 2548 participants) and the file BD < HC was pooled with the SCZ > HC one (pooled file containing 68 experiments, 97 foci, and 2548 participants). These outputs were then used as inputs for the contrast (second-level) analyses (Eickhoff et al., 2011). Parameters for these computations were set at p-value <0.05, p-value permutations = 2000 and minimum cluster volume = 50 mm³. This procedure operates at two levels, elaborating both statistically significant differences and overlapping elements between the two ALE datasets. Differences are the product of the direct subtraction of the second-level analyses datasets (ALE images) of each group from the other. Instead, similarities are the results of the conjunction generated with the voxel-wise minimum value of these ALE datasets (Sorella et al., 2021). Brain plots were generated with SurfIce (https://www.nitrc.org/plugins/mwiki/index.php/surfice:MainPage).

3. Results

In the present section, we describe the results obtained in four contrasts of interest from second-level analyses: overlapping hyper-/hypo-activation and distinct hyper-/hypo-activation. The analyses carried out in order to look for the presence of common abnormal alterations presenting a difference in the direction of activation across clinical groups (BD > HC ∧ SCZ < HC and BD < HC ∧ SCZ > HC) gave no results, and will not be discussed further in this section. See Supplementary Tables S2 and S3 for the first level analysis data (SCZ versus HC and BD versus HC).

3.1. Common imparments in SCZ and BD

The conjunction analysis included 68 experiments and 2548 participants, yielding a total of 132 foci of increased brain activity and 133 foci of decreased brain activity in SCZ and BD patients compared to HC. This analysis revealed the presence of three right lateralised overlapping clusters of common increased brain activity in SCZ and BD patients compared to HC, located in sub-lobar and limbic areas. The first and larger cluster (984 mm³) extends entirely in right Sub-lobar areas, specifically in the Lentiform Nucleus (94.3% Putamen, 5.7% Lateral Globus Pallidus). The second cluster presents a smaller size (264 mm³) and is located in its entirety in the right Limbic Lobe, in the Parahippocampal Gyrus (54.5% Brodmann area 35, 45.5% Brodmann area 28). The third cluster (216 mm³) is situated in right Sub-lobar areas, specifically it extends in the Lentiform Nucleus (61.1%) and in the Thalamus (38.9%) (in particular: 27.8% Lateral Globus Pallidus, 27.8% Ventral Lateral Nucleus, 11.1% Medial Globus Pallidus, 5.6% Ventral Anterior Nucleus). No significant clusters of decreased brain activity emerged from this analysis. See Fig. 1 and Table 2.

Fig. 1.

Fig. 1

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Brain functional abnormal activations in schizophrenia and bipolar patients. On the top (A), schizophrenics and bipolars common impairments (hyperactivations) are shown in magenta (ALE scores). In the middle (B), functional anomalies associated to schizophrenia (Z scores). In the bottom (C), functional anomalies associated to bipolar disorder (Z scores). In Fig. 1(B) and (C), hyperactivations are shown in the red-yellow gradient; hypoactivations are reported in the blue-green gradient. (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.)

Table 2.

Significant clusters presenting shared abnormal activations in schizophrenia and bipolar disorder compared to healthy controls.

Cluster Side
 Label (Nearest Gray Matter within 5 mm)
 BA
 Peaks (MNI)
 ALE Score
x y z
HYPERACTIVATIONS OVERLAP (SCZ > HC) ∧ (BD > HC)
1 R Sub-lobar, Lentiform Nucleus, Putamen 30 2 −6 0.009
2 R Limbic Lobe, Parahippocampal Gyrus 35 28 −26 −18 0.003
2 R Limbic Lobe, Parahippocampal Gyrus 35 24 −24 −18 0.003
3 R Sub-lobar, Lentiform Nucleus 16 2 2 0.003
3 R Sub-lobar, Thalamus, Ventral Lateral Nucleus 14 −8 2 0.003
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3.2. SCZ versus BD

The subtraction analysis between SCZ and BD groups included: (a) 41 experiments for the contrast SCZ vs HC and 1715 participants, yielding 50 foci of increased brain activity and 86 foci of decreased brain activity; (b) 27 experiments for the contrast BD vs HC and 833 participants, yielding a total of 82 foci of increased brain activity and 47 foci of decreased brain activity. For these analyses, each cluster peaks' details are reported in Table 3. See Fig. 1.

Table 3.

Significant clusters presenting different abnormal activations between schizophrenia and bipolar disorder compared to healthy controls.

Cluster Side
 Label (Nearest Gray Matter within 5 mm)
 BA
 Peaks (MNI)
 P
 Z
x y z
GREATER HYPERACTIVATION IN SCHIZOPHRENIA (SCZ > HC) – (BD > HC)
1 L Sub-lobar, Claustrum −34 −8 −12 0.006 2.543
1 L Limbic Lobe, Parahippocampal Gyrus, Hippocampus −32 −12 −16 0.007 2.484
1 L Limbic Lobe, Parahippocampal Gyrus, Hippocampus −36 −10 −16 0.009 2.366
1 L Limbic Lobe, Parahippocampal Gyrus, Hippocampus −34 −12 −22 0.011 2.308
1 L Limbic Lobe, Parahippocampal Gyrus, Amygdala −31.1 −5.3 −16.2 0.014 2.197
1 L Limbic Lobe, Parahippocampal Gyrus, Hippocampus −28 −12 −27 0.015 2.183
1 L Limbic Lobe, Parahippocampal Gyrus, Amygdala −26 −12 −21 0.015 2.170
1 L Limbic Lobe, Parahippocampal Gyrus, Amygdala −22 −7 −18 0.016 2.144
1 L Limbic Lobe, Parahippocampal Gyrus, Amygdala −29.5 −1.5 −20 0.018 2.108
1 L Limbic Lobe, Parahippocampal Gyrus, Amygdala −28 −4.7 −24 0.025 1.969
1 L Temporal Lobe, Superior Temporal Gyrus 38 −33 2 −19 0.038 1.780
1 L Limbic Lobe, Parahippocampal Gyrus, Amygdala −31.3 −2.1 −27.6 0.025 1.969
2 R Limbic Lobe, Uncus, Amygdala 32.5 −6.6 −28.5 0.025 1.969
2 R Limbic Lobe, Uncus, Amygdala 26.8 −2.8 −28.4 0.025 1.969
2 R Limbic Lobe, Uncus 28 30 4 −26 0.023 1.995
2 R Limbic Lobe, Uncus 28 36.6 −1 −31.3 0.025 1.969
2 R Limbic Lobe, Parahippocampal Gyrus, Amygdala 26.7 −1.3 −23 0.042 1.734
3 L Temporal Lobe, Fusiform Gyrus 20 −38.9 −37 −18.9 0.024 1.986
3 L Limbic Lobe, Parahippocampal Gyrus 36 −35.3 −33 −20 0.040 1.757
3 L Temporal Lobe, Fusiform Gyrus 36 −43 −36 −25 0.046 1.690
GREATER HYPOACTIVATION IN SCHIZOPHRENIA (SCZ < HC) – (BD < HC)
1 L Limbic Lobe, Parahippocampal Gyrus 28 −22 −22 −24 0.024 1.986
1 L Limbic Lobe, Parahippocampal Gyrus 35 −20 −26 −26 0.025 1.960
1 L Limbic Lobe, Parahippocampal Gyrus 35 −26 −22 −25 0.034 1.832
2 L Brainstem, Midbrain, Substantia Nigra −8 −22 −14 0.026 1.943
2 L Brainstem, Midbrain, Red Nucleus −8 −22 −10 0.03 1.881
GREATER HYPOACTIVATION IN BIPOLAR DISORDER (BD < HC) – (SCZ < HC)
1 R Frontal Lobe, Precentral Gyrus 6 38.7 10.2 28 0.007 2.484
1 R Frontal Lobe, Middle Frontal Gyrus 9 46 18 26 0.025 1.960
1 R Frontal Lobe, Inferior Frontal Gyrus 9 46 18 22 0.009 2.387
1 R Frontal Lobe, Precentral Gyrus 9 45.3 12.9 29.3 0.01 2.326
1 R No Gray Matter found 34.3 12 35 0.011 2.290
1 R Frontal Lobe, Inferior Frontal Gyrus 9 48 16 18 0.029 1.896
1 R Frontal Lobe, Precentral Gyrus 9 44 24 35 0.031 1.866
1 R Frontal Lobe, Precentral Gyrus 9 39.2 13.5 34.2 0.007 2.484
1 R Frontal Lobe, Inferior Frontal Gyrus 9 50 10 20 0.046 1.690
1 R No Gray Matter found 28.8 7.8 35 0.048 1.670
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This analysis revealed three clusters of increased brain activity which specifically characterise SCZ. The first cluster (3632 mm³) is located in the left Limbic Lobe: it includes the Parahippocampal Gyrus (87.9%) and the Uncus (12.1%) (in particular: 87.9% Amygdala, 9.9% Hippocampus, 2.1% Brodmann area 34). The second cluster (2560 mm³) includes the right Limbic Lobe (93.3%) and the right Temporal Lobe (6.7%). Specifically, it includes the Uncus (58.3%), the Parahippocampal Gyrus (35%), the Fusiform Gyrus (3.3%) and the Middle Temporal Gyrus (3.3%) (in particular: 58.3% Amygdala, 30% Brodmann area 28, 5% Brodmann area 34, 3.3% Brodmann area 20, 3.3% Brodmann area 21). The third and last cluster (608 mm³) is located in left Cerebrum (97.6%) and left Cerebellum (2.4%). It includes the Limbic Lobe (58.5%), the Temporal Lobe (39%) and the Anterior Lobe (2.4%). Specifically, it includes the Parahippocampal Gyrus (58.5%), the Fusiform Gyrus (39%) and the Culmen (2.4%) (in particular: 63.4% Brodmann area 36, 34.1% Brodmann area 20).

The subtraction analysis also revealed two clusters of decreased brain activity specific for SCZ. The first cluster (760 mm³) is located in the left Limbic Lobe, specifically in the Parahippocampal Gyrus (73.1% Brodmann area 35, 26.9% Brodmann area 28). The second cluster (64 mm³) includes the left Midbrain (100% Substantia Nigra).

Finally, a significant right lateralised cluster of decreased brain activity specific for BD was found. This cluster (4272 mm³) is located in the right Frontal Lobe. Specifically, it includes the Precentral Gyrus (66.7%), the Inferior Frontal Gyrus (18.3%), and the Middle Frontal Gyrus (15%) (in particular: 90% Brodmann area 9, 10% Brodmann area 6).

No significant clusters of increased brain activity which characterise BD were found.

4. Discussion

Despite the still in use dichotomous division of schizophrenia (SCZ) and bipolar disorder (BD), several findings questioned this position and led to think about the existence of a continuum between these conditions (Crow, 1986; Möller, 2003; Sorella et al., 2019). The present study focused on the affective dimension of the expanded continuum hypothesis (Sorella et al., 2019), and aimed to identify similarities and differences in this domain by using a coordinate-based meta-analytic approach of neuroimaging studies. Specifically, we focused on studies that investigated the perception of negative visual stimuli in SCZ and BD. Taking into consideration the literature reviewed, this is the first meta-analysis that directly compares abnormal brain activations of SCZ and BD patients during the visual perception of negative emotional stimuli.

4.1. Overlap between schizophrenia and bipolar disorder

We found shared abnormalities between SCZ and BD in brain functioning involved in the perception and elaboration of negative stimuli. Specifically, the conjunction analysis revealed an increased activity in limbic areas such as the parahippocampal gyrus and the thalamus, but also in sub-lobar areas such as the lentiform nucleus (putamen and lateral globus pallidus). In line with our results, there is evidence about the involvement of the parahippocampal gyrus in complex emotional processes, in particular for processing negative (see the review by Lew and Semendeferi, 2017) and uncertain stimuli (M. Zhang et al., 2016). Lesions in this area may lead to a decrement in sensitivity towards negative stimuli such as unpleasant music (Gosselin et al., 2006). Also, functional abnormalities in BD have been found in fronto-limbic structures involved in impulsivity, mood regulation, and emotion processing, including the hippocampal and parahippocampal cortex (Almeida et al., 2009; Delvecchio et al., 2013; Fleck et al., 2011; Houenou et al., 2011; Price and Drevets, 2012; Teng et al., 2014). Functional alterations in hippocampal-parahippocampal areas have been widely reported by previous meta-analyses on emotion processing in SCZ (Delvecchio et al., 2013; Dong et al., 2018; H. J. Li et al., 2010; Taylor et al., 2012). Interestingly, parahippocampal/hippocampal functional anomalies have been reported also in individuals at risk of psychosis during affective tasks (Bourque et al., 2017; O'Brien et al., 2020), and the reduced thickness of parahippocampal gyri has been identified as associated with genetic risk of both BD and SCZ (Hulshoff Pol et al., 2012).

Concerning the right thalamus, a meta-analysis on face emotion processing in healthy individuals reported its activation during disgusted faces processing when contrasted with neutral faces (Fusar-Poli et al., 2009). Furthermore, thalamic lesions are associated with impairments in facial affect recognition and poorer performance in sadness identification (Cheung et al., 2006). Higher activity in the thalamus may then represent heightened sensorial processing of visual negative stimuli (Delvecchio et al., 2012). Indeed, this area is involved in information processing (Castro-Alamancos, 2004; Pessoa, 2017) and it is more active during emotional scene processing when contrasted to neutral images (Sabatinelli et al., 2011). Thalamic morphometric alterations have been reported in both BD I patients not treated with lithium (Radenbach et al., 2010) and SCZ patients (Haijma et al., 2013; Wright et al., 2000). Further, even when directly compared, SCZ and BD revealed shared structural abnormalities in the thalamus (Dasari et al., 1999; Lee et al., 2020; McIntosh et al., 2004), confirming its involvement in both pathologies. Interestingly, antipsychotic drugs - that are often taken in both disorders - seem to act on the cellular activation in the thalamus (Cohen et al., 1998, 2003), and increase the volume of this area (Dazzan et al., 2005; Gur et al., 1998).

Both the thalamus and basal ganglia play an important role in the reward circuit (Haber and Knutson, 2010; Lapomarda et al., 2021a, 2021b) In addition, basal ganglia are involved in affective processes (Péron et al., 2017). In fact, alterations in this region are associated with abnormal global face emotion recognition and with difficulties in the identification of fearful, disgusted, and angry faces (Cheung et al., 2006). Moreover, the basal ganglia calcification can be associated with pathological mood alterations and psychosis (Johnson et al., 2013). Accordingly, evidence exists about structural anomalies of basal ganglia at early stage of BD (Strakowski et al., 2005), as well as shape alterations in BD (Hwang et al., 2006). Similarly, SCZ patients show altered basal ganglia volume and shape (Hirjak et al., 2015; Mamah et al., 2007; van Erp et al., 2016). These alterations seem to play a role in impulsivity as well, when considering dysfunctions in the interplay of cortical-limbic structures (e.g. anterior cingulate cortex, basal ganglia, insula) (Brown et al., 2006; Lapomarda et al., 2021a, 2021b). Finally, dysregulation of dopaminergic neurons in the basal ganglia (Haber, 2014) seems to cause an excessive salience attribution to neutral stimuli (see the review by Howes and Kapur, 2009), playing a crucial role in the psychotic manifestations of both disorders (for reviews see Kapur, 2003; Seeman and Kapur, 2000; Strakowski, 2014; Toda and Abi-Dargham, 2007; Walderhaug et al., 2011).

To sum up, we found that during the visual processing of negative valenced emotional stimuli both SCZ and BD patients show hyperactivation in the parahippocampal area, which is involved in the processing of uncertain (M. Zhang et al., 2016) and negative stimuli (see Lew and Semendeferi, 2017), as well as in basal ganglia and thalamus, which are usually implied in mood, emotional process and reward processing (Buhle et al., 2014; Haber and Knutson, 2010; Hsu et al., 2014; Johnson et al., 2013). As a matter of fact, abnormalities in this circuit have been linked to emotional disturbances (Delvecchio et al., 2012), as parahippocampal lesions can lead to decreased sensitivity towards unpleasant stimuli, perceiving them as pleasant (Gosselin et al., 2006) and thalamic and basal ganglia hyperactivation seem to amplify the processing of emotionally salient stimuli (Delvecchio et al., 2012). Abnormal salience attribution has been previously related to psychosis (for a review see Kapur, 2003). Taken together, our conjunction analysis data suggest that both BD and SCZ patients are characterised by an altered processing of negative emotional stimuli that leads them to elaborate negative cues as more unpleasant and intense than healthy participants.

4.2. Differences between schizophrenia and bipolar disorder: schizophrenia functional abnormalities

Despite the commonalities, our data also suggest some specificities for each disorder. SCZ patients display higher activations in subcortical areas, such as the amygdala, hippocampus and parahippocampal area, uncus and claustrum when compared to both HC and BD. Previous studies suggest the involvement of the amygdala in several aspects of negative emotions (such as emotional learning, response, recognition), social cognition, and social behaviour (reviewed in Schumann et al., 2011). Beside the well-known association with fear and anxiety (Fox and Shackman, 2019; Grecucci et al., 2020a; Sah, 2017; Saviola et al., 2020), the amygdala has been associated with other negative emotions, such as shame (Grecucci et al., 2021; Piretti et al., 2020), anger perception (Sorella et al., 2021), and sad or fearful facial expressions processing (Fusar-Poli et al., 2009). Alterations in the amigdalo-hippocampal complex have been reported to be associated with schizophrenia neurodevelopment and emotional dysfunctions (Aleman and Kahn, 2005; Bolton et al., 2012; Ganzola et al., 2014; Taylor et al., 2012).

Our meta-analysis shows that claustrum may play a role in the symptomatology of the SCZ. In a review, Crick and Koch (2005) speculated that the claustrum might be involved in consciousness (see also Koubeissi et al., 2014). Claustral white matter alterations in SCZ patients have been associated with symptoms unawareness (Antonius et al., 2011). Furthermore, claustral structural abnormalities have been detected in SCZ (Bernstein et al., 2016), in particular in patients with more severe delusional symptoms (Cascella et al., 2011). This area seems also to play a role in hallucinations during the administration of hallucinatory drugs or in Parkinson disease (for a review see Smith et al., 2020), and claustral white matter excess has been found in SCZ patients with hallucinations when compared with the ones that do not present this symptom (Shapleske et al., 2002). Also, a meta-analytic study reported that the claustrum activates during auditory verbal hallucinations (van Lutterveld et al., 2013). Interestingly, claustral functional hyperactivations have also been reported in SCZ patients with severe negative symptoms (Galeno et al., 2004).

Moreover, hyperactivation of the left superior temporal gyrus and the left fusiform gyrus seems to distinguish SCZ from BD. Beside their relevance for emotions (Vytal and Hamann, 2010), the left superior temporal gyrus has been linked to severe auditory verbal hallucinations in SCZ (Modinos et al., 2013). Similarly, some data suggest the involvement of the uncus in hallucinations (Fortuna et al., 2001; Roberts et al., 2001). The left parahippocampal area is involved in hallucinations as well, as its deactivation seems to predict auditory verbal hallucinations in SCZ (Diederen et al., 2010).

In sum, these results highlight the presence of specific increased activations in SCZ in regions that are not limited to the emotional domain (Bora and Pantelis, 2016; Goghari and Sponheim, 2013; Yalcin-Siedentopf et al., 2014), but that are also involved in other cognitive domains and psychotic manifestations proper to the disorder (for a review see Waters et al., 2012; Sorella et al., 2019).

4.3. Differences between schizophrenia and bipolar disorder: bipolar disorder functional abnormalities

Differently from SCZ, activation abnormalities in BD were mainly localised in the medial and dorsolateral prefrontal cortex (Brodmann, 1909, cited in Catani, 2019). These areas are involved in emotional appraisal (Ninivaggi, 2020), emotion regulation (Grecucci et al., 2019; Grecucci et al., 2020b; Hou et al., 2017; Messina et al., 2015; Pappaianni et al., 2020; Pozzi et al., 2021; Sulpizio et al., 2021), and response inhibition (Aron et al., 2003; Chikazoe et al., 2007; Jacobson et al., 2011).

Abnormal activation in the right inferior frontal gyrus (IFG) and in the right middle frontal gyrus (MFG) in BD patients has been previously reported in a meta-analysis on response inhibition (Hajek et al., 2013; see also Stefanopoulou et al., 2009). Interestingly, abnormal response inhibition has been proposed to be the most relevant cognitive endophenotype of bipolar disorder (Bora et al., 2009; Lapomarda et al., 2021a, 2021b).

Several data support the role of IFG, MFG and precentral gyrus in emotion regulation in healthy population (Brandl et al., 2019; Frank et al., 2014; Grecucci et al., 2013; Kohn et al., 2014; Morawetz et al., 2017). Emotion regulation aberrations associated to frontal anomalies have been frequently reported in BD (Kanske et al., 2015; Khafif et al., 2021; Sankar et al., 2021; L. L. Zhang et al., 2018). In their review, Kurtz et al. (2021) identified emotion regulation deficits as a potential marker of BD risk.

4.4. General discussion

The present study aimed to better characterise similarities and differences between SCZ and BD in the affective dimension of the expanded continuum hypothesis conceptualised by Sorella et al. (2019). By analysing studies about the visual perception of negative stimuli, we were able to point out shared abnormalities in a thalamic-parahippocampal-basal ganglia circuit. According to the above-mentioned literature, this circuit may be responsible for the greater sensitivity, heightened sensorial perception, and excessive salience attribution towards emotional stimuli proper to the two disorders. This result supports the hypothesis of shared abnormalities between SCZ and BD in the affective domain, specifically that of emotion perception, providing further evidence about the existence of an expanded continuum (Sorella et al., 2019).

Nevertheless, this study also highlighted differences between patients. On one hand, SCZ patients show specific functional aberrations in areas possibly involved in emotional processing, emotional memory, and hallucinations. On the other hand, BD patients show abnormalities in regions known to be crucial for emotion regulation and response inhibition. When considering affective and emotional brain regions mainly involved in perceptual, psychotic and memory processes, our data seem to support a greater impairment in SCZ. On the other hand, when considering affective brain regions mainly involved in emotion regulation and response inhibition, our analysis shows stronger difficulties of BD patients. These results further support the expanded continuum hypothesis (Sorella et al., 2019), sustaining the presence of more severe affective dysfunctions in BD, but framing them in the domain of emotion regulation.

5. Limitations

Our results should be considered in the light of some limitations. Starting from the selection criteria, the choice of including studies that considered emotional stimuli in contrast to their neutral counterparts (neutral faces and neutral IAPS) led us to exclude several studies that took into account different modalities (e.g., fixation cross, blank screen, shapes, scrambled or erased faces) as control condition. Nevertheless, this conservative criterion allowed us to reduce the presence of noise in the data. Another limitation is that the use of neutral faces as control condition has been questioned in light of the fact that these stimuli seem to be abnormally processed and misperceived by both schizophrenia and bipolar disorder patients when compared to healthy controls (Filkowski and Haas, 2017): instead of being perceived as a non-emotional baseline, neutral faces seem to be elaborated as fearful/angry by schizophrenics, while bipolars seem to process them as sad, showing a “negativity bias” towards neutral faces (reviewed by Filkowski and Haas, 2017).

Furthermore, previous studies suggested that BD I and BD II patients mildly differ in emotion perception (Derntl et al., 2009; B. B. Zhang et al., 2018) and emotion regulation (Caseras et al., 2015) from both behavioural and neural point of view. Unfortunately, our meta-analysis included only few BPD II patients due to the lack of studies focusing on this category of patients. In addition, in our selection criteria and analyses we did not consider the mood state nor the symptomatology of both patients’ groups. According to the emotional-response model elaborated by Bigot et al., in 2020, BD mood states play a crucial role in emotional processing, in particular in valence and intensity attribution. Also, the remitted and symptomatic states of SCZ patients seem to have consequences on emotion recognition (Maat et al., 2015).

To conclude, even though the present study results are novel and relevant, our meta-analysis did not include studies on emotion regulation: further studies on this topic are necessary to deepen it and test further the continuum hypothesis.

6. Conclusion

The present study aimed to shed light on the affective dimension of the expanded continuum hypothesised by Sorella et al. (2019) between schizophrenia (SCZ) and bipolar disorder (BD). To address this issue, we applied a coordinate-based meta-analytic approach to neuroimaging data. Our results highlight the presence of shared abnormal right-sided circuit in limbic and sub-lobar areas for both classes of patients. This data provides evidence for common alterations between SCZ and BD during the visual processing of negative stimuli. Nevertheless, the presence of distinct functional brain abnormalities – mainly located in the brainstem, limbic, temporal, and sub-lobar areas in SCZ, and in right frontal areas in BD – suggests that these pathological conditions diverge in some aspects of emotion perception. To conclude, our results provided new fresh knowledge on the affective dimension of the expanded continuum hypothesis. Future studies are needed to further understand the similarities and differences between these two complex disorders, in light of the affective, psychotic and cognitive dimensions of the continuum. Such findings may help better characterise similarities and differences between BD and SCZ for what concerns the affective side of the continuum hypothesis, possibly improving future diagnostic evaluations and personalised treatments of these disorders.

Open practices statement

The meta-analysis data and materials are available at https://osf.io/cya9h/?view_only=0985efa9eca448b5a417a582c1b39c3d. The experiment was not preregistered.

Funding

This research did not receive any fund, grant or other support from funding agencies in the public, commercial, or not-for-profit sectors.

Authors’ contribution

AG, CO, IM: study design, analyses, figures, paper writing. GL, SS, paper writing, editing of the final version.

Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Footnotes

Appendix A

Supplementary data to this article can be found online at https://doi.org/10.1016/j.ynirp.2023.100173.

Contributor Information

Alessandro Grecucci, Email: alessandro.grecucci@unitn.it.

Chiara Orsini, Email: chiara.orsini@uibk.ac.at.

Gaia Lapomarda, Email: gl2508@nyu.edu.

Sara Sorella, Email: sara.sorella@unitn.it.

Irene Messina, Email: irene-messina@hotmail.com.

Appendix A. Supplementary data

The following is the Supplementary data to this article.

Multimedia component 1
mmc1.docx (48KB, docx)

Data availability

Analized data can be found following the link reported in the section "Open practices statement". The MRI coordinates used can be found in the papers reported in Table 1

References

  1. Ahmadi L., Kazemi Nezhad S.R., Behbahani P., Khajeddin N., Pourmehdi-Boroujeni M. Genetic variations ofDAOA (rs947267 and rs3918342) and COMT genes (rs165599 and rs4680) in schizophrenia and bipolar I disorder. Basic Clin. Neurosci. 2018;9(6):429–438. doi: 10.32598/bcn.9.6.429. [DOI] [PMC free article] [PubMed] [Google Scholar]
  2. Aleman A., Kahn R.S. Strange feelings: do amygdala abnormalities dysregulate the emotional brain in schizophrenia? Prog. Neurobiol. 2005;77(5):283–298. doi: 10.1016/j.pneurobio.2005.11.005. [DOI] [PubMed] [Google Scholar]
  3. Almeida J.R., Mechelli A., Hassel S., Versace A., Kupfer D.J., Phillips M.L. Abnormally increased effective connectivity between parahippocampal gyrus and ventromedial prefrontal regions during emotion labeling in bipolar disorder. Psychiatr. Res. 2009;174(3):195–201. doi: 10.1016/j.pscychresns.2009.04.015. [DOI] [PMC free article] [PubMed] [Google Scholar]
  4. American Psychiatric Association . 5th Ed. 2013. Diagnostic and Statistical Manual of Mental Disorders. [DOI] [Google Scholar]
  5. Ancín I., Cabranes J.A., Santos J.L., Sánchez-Morla E., Barabash A. Executive deficits: a continuum schizophrenia-bipolar disorder or specific to schizophrenia? J. Psychiatr. Res. 2013;47(11):1564–1571. doi: 10.1016/j.jpsychires.2013.07.008. [DOI] [PubMed] [Google Scholar]
  6. Angst J. Historical aspects of the dichotomy between manic-depressive disorders and schizophrenia. Schizophr. Res. 2002;57(1):5–13. doi: 10.1016/s0920-9964(02)00328-6. [DOI] [PubMed] [Google Scholar]
  7. Anticevic A., Repovs G., Corlett P.R., Barch D.M. Negative and nonemotional interference with visual working memory in schizophrenia. Biol. Psychiatr. 2011;70(12):1159–1168. doi: 10.1016/j.biopsych.2011.07.010. [DOI] [PubMed] [Google Scholar]
  8. Antonius D., Prudent V., Rebani Y., D'Angelo D., Ardekani B.A., Malaspina D., Hoptman M.J. White matter integrity and lack of insight in schizophrenia and schizoaffective disorder. Schizophr. Res. 2011;128(1–3):76–82. doi: 10.1016/j.schres.2011.02.020. [DOI] [PMC free article] [PubMed] [Google Scholar]
  9. Aron A.R., Fletcher P.C., Bullmore E.T., Sahakian B.J., Robbins T.W. Stop-signal inhibition disrupted by damage to right inferior frontal gyrus in humans. Nat. Neurosci. 2003;6(2):115–116. doi: 10.1038/nn1003. [DOI] [PubMed] [Google Scholar]
  10. Au R., Xiang Y.T., Ungvari G.S., Lee E., Shum D., Man D., Tang W.K. Prospective memory performance in persons with schizophrenia and bipolar disorder and healthy persons. Psychiatr. Care. 2017;53(4):266–274. doi: 10.1111/ppc.12172. [DOI] [PubMed] [Google Scholar]
  11. Badner J.A., Gershon E.S. Meta-analysis of whole-genome linkage scans of bipolar disorder and schizophrenia. Mol. Psychiatr. 2002;7(4):405–411. doi: 10.1038/sj.mp.4001012. [DOI] [PubMed] [Google Scholar]
  12. Becerril K., Barch D. Influence of emotional processing on working memory in schizophrenia. Schizophr. Bull. 2011;37(5):1027–1038. doi: 10.1093/schbul/sbq009. [DOI] [PMC free article] [PubMed] [Google Scholar]
  13. Berman N.G., Parker R.A. Meta-analysis: neither quick nor easy. BMC Med. Res. Methodol. 2002;2:10. doi: 10.1186/1471-2288-2-10. [DOI] [PMC free article] [PubMed] [Google Scholar]
  14. Bermpohl F., Dalanay U., Kahnt T., Sajonz B., Heimann H., Ricken R., Stoy M., Hägele C., Schlagenhauf F., Adli M., Wrase J., Ströhle A., Heinz A., Bauer M. A preliminary study of increased amygdala activation to positive affective stimuli in mania. Bipolar Disord. 2009;11(1):70–75. doi: 10.1111/j.1399-5618.2008.00648.x. [DOI] [PubMed] [Google Scholar]
  15. Bernstein H.G., Ortmann A., Dobrowolny H., Steiner J., Brisch R., Gos T., Bogerts B. Bilaterally reduced claustral volumes in schizophrenia and major depressive disorder: a morphometric postmortem study. Eur. Arch. Psychiatr. Clin. Neurosci. 2016;266(1):25–33. doi: 10.1007/s00406-015-0597-x. [DOI] [PubMed] [Google Scholar]
  16. Bigot M., Alonso M., Houenou J., Sarrazin S., Dargél A.A., Lledo P.M., Henry C. An emotional-response model of bipolar disorders integrating recent findings on amygdala circuits. Neurosci. Biobehav. Rev. 2020;118:358–366. doi: 10.1016/j.neubiorev.2020.07.037. [DOI] [PubMed] [Google Scholar]
  17. Bodnar M., Harvey P.O., Malla A.K., Joober R., Lepage M. The parahippocampal gyrus as a neural marker of early remission in first-episode psychosis: a voxel-based morphometry study. Clin. Schizophrenia Relat. Psychoses. 2011;4(4):217–228. doi: 10.3371/CSRP.4.4.2. [DOI] [PubMed] [Google Scholar]
  18. Bolton M.M., Heaney C.F., Sabbagh J.J., Murtishaw A.S., Magcalas C.M., Kinney J.W. Deficits in emotional learning and memory in an animal model of schizophrenia. Behav. Brain Res. 2012;233(1):35–44. doi: 10.1016/j.bbr.2012.04.049. [DOI] [PMC free article] [PubMed] [Google Scholar]
  19. Bora E., Pantelis C. Meta-analysis of cognitive impairment in first-episode bipolar disorder: comparison with first-episode schizophrenia and healthy controls. Schizophr. Bull. 2015;41(5):1095–1104. doi: 10.1093/schbul/sbu198. [DOI] [PMC free article] [PubMed] [Google Scholar]
  20. Bora E., Pantelis C. Social cognition in schizophrenia in comparison to bipolar disorder: a meta-analysis. Schizophr. Res. 2016;175(1–3):72–78. doi: 10.1016/j.schres.2016.04.018. [DOI] [PubMed] [Google Scholar]
  21. Bora E., Yucel M., Pantelis C. Cognitive endophenotypes of bipolar disorder: a meta-analysis of neuropsychological deficits in euthymic patients and their first-degree relatives. J. Affect. Disord. 2009;113(1–2):1–20. doi: 10.1016/j.jad.2008.06.009. [DOI] [PubMed] [Google Scholar]
  22. Bourque J., Spechler P.A., Potvin S., Whelan R., Banaschewski T., Bokde A., Bromberg U., Büchel C., Quinlan E.B., Desrivières S., Flor H., Frouin V., Gowland P., Heinz A., Ittermann B., Martinot J.L., Paillère-Martinot M.L., McEwen S.C., Nees F., Orfanos D.P., et al. Functional neuroimaging predictors of self-reported psychotic symptoms in adolescents. Am. J. Psychiatr. 2017;174(6):566–575. doi: 10.1176/appi.ajp.2017.16080897. [DOI] [PMC free article] [PubMed] [Google Scholar]
  23. Bowen H.J., Kark S.M., Kensinger E.A. NEVER forget: negative emotional valence enhances recapitulation. Psychon. Bull. Rev. 2018;25(3):870–891. doi: 10.3758/s13423-017-1313-9. [DOI] [PMC free article] [PubMed] [Google Scholar]
  24. Brandl F., Le Houcq Corbi Z., Mulej Bratec S., Sorg C. Cognitive reward control recruits medial and lateral frontal cortices, which are also involved in cognitive emotion regulation: a coordinate-based meta-analysis of fMRI studies. Neuroimage. 2019;200:659–673. doi: 10.1016/j.neuroimage.2019.07.008. [DOI] [PubMed] [Google Scholar]
  25. Brett M., Lancaster J., Christoff K. Using the MNI brain with the Talairach atlas. Neuroimage. 2001;13:S85. [Google Scholar]
  26. Brett M., Johnsrude I.S., Owen A.M. The problem of functional localization in the human brain. Nat. Rev. Neurosci. 2002;3(3):243–249. doi: 10.1038/nrn756. [DOI] [PubMed] [Google Scholar]
  27. Brodmann K. Barth; 1909. Vergleichende Lokalisationslehre der Grosshirnrinde in ihren Prinzipien dargestellt auf Grund des Zellenbaues. [Google Scholar]
  28. Brown S.M., Manuck S.B., Flory J.D., Hariri A.R. Neural basis of individual differences in impulsivity: contributions of corticolimbic circuits for behavioral arousal and control. Emotion. 2006;6(2):239–245. doi: 10.1037/1528-3542.6.2.239. [DOI] [PubMed] [Google Scholar]
  29. Buhle J.T., Silvers J.A., Wager T.D., Lopez R., Onyemekwu C., Kober H., Weber J., Ochsner K.N. Cognitive reappraisal of emotion: a meta-analysis of human neuroimaging studies. Cerebral Cortex (New York, N.Y.: 1991) 2014;24(11):2981–2990. doi: 10.1093/cercor/bht154. [DOI] [PMC free article] [PubMed] [Google Scholar]
  30. Caletti E., Paoli R.A., Fiorentini A., Cigliobianco M., Zugno E., Serati M., Orsenigo G., Grillo P., Zago S., Caldiroli A., Prunas C., Giusti F., Consonni D., Altamura A.C. Neuropsychology, social cognition and global functioning among bipolar, schizophrenic patients and healthy controls: preliminary data. Front. Hum. Neurosci. 2013;7:661. doi: 10.3389/fnhum.2013.00661. [DOI] [PMC free article] [PubMed] [Google Scholar]
  31. Cascella N.G., Gerner G.J., Fieldstone S.C., Sawa A., Schretlen D.J. The insula-claustrum region and delusions in schizophrenia. Schizophr. Res. 2011;133(1–3):77–81. doi: 10.1016/j.schres.2011.08.004. [DOI] [PubMed] [Google Scholar]
  32. Caseras X., Murphy K., Lawrence N.S., Fuentes-Claramonte P., Watts J., Jones D.K., Phillips M.L. Emotion regulation deficits in euthymic bipolar I versus bipolar II disorder: a functional and diffusion-tensor imaging study. Bipolar Disord. 2015;17(5):461–470. doi: 10.1111/bdi.12292. [DOI] [PMC free article] [PubMed] [Google Scholar]
  33. Castro-Alamancos M.A. Dynamics of sensory thalamocortical synaptic networks during information processing states. Prog. Neurobiol. 2004;74(4):213–247. doi: 10.1016/j.pneurobio.2004.09.002. [DOI] [PubMed] [Google Scholar]
  34. Catani M. In: The Frontal Lobes. 163, 3rd series. D'Esposito M., Grafman J.H., editors. Elsevier; 2019. The anatomy of the human frontal lobe; pp. 95–122. (Handbook of Clinical Neurology). [DOI] [PubMed] [Google Scholar]
  35. Cerullo M.A., Eliassen J.C., Smith C.T., Fleck D.E., Nelson E.B., Strawn J.R., Lamy M., DelBello M.P., Adler C.M., Strakowski S.M. Bipolar I disorder and major depressive disorder show similar brain activation during depression. Bipolar Disord. 2014;16(7):703–712. doi: 10.1111/bdi.12225. [DOI] [PMC free article] [PubMed] [Google Scholar]
  36. Champagne J., Lakis N., Bourque J., Stip E., Lipp O., Mendrek A. Progesterone and cerebral function during emotion processing in men and women with schizophrenia. Schizophr. Res. Treatment. 2012 doi: 10.1155/2012/917901. 2012. [DOI] [PMC free article] [PubMed] [Google Scholar]
  37. Chen C.H., Lennox B., Jacob R., Calder A., Lupson V., Bisbrown-Chippendale R., Suckling J., Bullmore E. Explicit and implicit facial affect recognition in manic and depressed States of bipolar disorder: a functional magnetic resonance imaging study. Biol. Psychiatr. 2006;59(1):31–39. doi: 10.1016/j.biopsych.2005.06.008. [DOI] [PubMed] [Google Scholar]
  38. Chen C.H., Suckling J., Lennox B.R., Ooi C., Bullmore E.T. A quantitative meta-analysis of fMRI studies in bipolar disorder. Bipolar Disord. 2011;13(1):1–15. doi: 10.1111/j.1399-5618.2011.00893.x. [DOI] [PubMed] [Google Scholar]
  39. Cheon E.J., Bearden C.E., Sun D., Ching C., Andreassen O.A., Schmaal L., Veltman D.J., Thomopoulos S.I., Kochunov P., Jahanshad N., Thompson P.M., Turner J.A., van Erp T. Cross disorder comparisons of brain structure in schizophrenia, bipolar disorder, major depressive disorder, and 22q11.2 deletion syndrome: a review of ENIGMA findings. Psychiatr. Clin. Neurosci. 2022;76(5):140–161. doi: 10.1111/pcn.13337. [DOI] [PMC free article] [PubMed] [Google Scholar]
  40. Cheung C.C., Lee T.M., Yip J.T., King K.E., Li L.S. The differential effects of thalamus and basal ganglia on facial emotion recognition. Brain Cognit. 2006;61(3):262–268. doi: 10.1016/j.bandc.2006.01.008. [DOI] [PubMed] [Google Scholar]
  41. Chikazoe J., Konishi S., Asari T., Jimura K., Miyashita Y. Activation of right inferior frontal gyrus during response inhibition across response modalities. J. Cognit. Neurosci. 2007;19(1):69–80. doi: 10.1162/jocn.2007.19.1.69. [DOI] [PubMed] [Google Scholar]
  42. Cohen B.M., Wan W., Froimowitz M.P., Ennulat D.J., Cherkerzian S., Konieczna H. Activation of midline thalamic nuclei by antipsychotic drugs. Psychopharmacology. 1998;135(1):37–43. doi: 10.1007/s002130050483. [DOI] [PubMed] [Google Scholar]
  43. Cohen B.M., Cherkerzian S., Ma J., Ye N., Wager C., Lange N. Cells in midline thalamus, central amygdala, and nucleus accumbens responding specifically to antipsychotic drugs. Psychopharmacology. 2003;167(4):403–410. doi: 10.1007/s00213-003-1423-0. [DOI] [PubMed] [Google Scholar]
  44. Collins D.L., Neelin P., Peters T.M., Evans A.C. Automatic 3D intersubject registration of MR volumetric data in standardized Talairach space. J. Comput. Assist. Tomogr. 1994;18(2):192–205. [PubMed] [Google Scholar]
  45. Craddock N., O'Donovan M.C., Owen M.J. The genetics of schizophrenia and bipolar disorder: dissecting psychosis. J. Med. Genet. 2005;42(3):193–204. doi: 10.1136/jmg.2005.030718. [DOI] [PMC free article] [PubMed] [Google Scholar]
  46. Crick F.C., Koch C. What is the function of the claustrum? Philos. Trans. R. Soc. Lond. Ser. B Biol. Sci. 2005;360(1458):1271–1279. doi: 10.1098/rstb.2005.1661. [DOI] [PMC free article] [PubMed] [Google Scholar]
  47. Crow T.J. The continuum of psychosis and its implication for the structure of the gene. Br. J. Psychiatry : J. Ment. Sci. 1986;149:419–429. doi: 10.1192/bjp.149.4.419. [DOI] [PubMed] [Google Scholar]
  48. Das P., Kemp A.H., Flynn G., Harris A.W., Liddell B.J., Whitford T.J., Peduto A., Gordon E., Williams L.M. Functional disconnections in the direct and indirect amygdala pathways for fear processing in schizophrenia. Schizophr. Res. 2007;90(1–3):284–294. doi: 10.1016/j.schres.2006.11.023. [DOI] [PubMed] [Google Scholar]
  49. Dasari M., Friedman L., Jesberger J., Stuve T.A., Findling R.L., Swales T.P., Schulz S.C. A magnetic resonance imaging study of thalamic area in adolescent patients with either schizophrenia or bipolar disorder as compared to healthy controls. Psychiatr. Res. 1999;91(3):155–162. doi: 10.1016/s0925-4927(99)00028-1. [DOI] [PubMed] [Google Scholar]
  50. Dazzan P., Morgan K.D., Orr K., Hutchinson G., Chitnis X., Suckling J., Fearon P., McGuire P.K., Mallett R.M., Jones P.B., Leff J., Murray R.M. Different effects of typical and atypical antipsychotics on grey matter in first episode psychosis: the AESOP study. Neuropsychopharmacology. 2005;30(4):765–774. doi: 10.1038/sj.npp.1300603. [DOI] [PubMed] [Google Scholar]
  51. Delvecchio G., Fossati P., Boyer P., Brambilla P., Falkai P., Gruber O., Hietala J., Lawrie S.M., Martinot J.L., McIntosh A.M., Meisenzahl E., Frangou S. Common and distinct neural correlates of emotional processing in Bipolar Disorder and Major Depressive Disorder: a voxel-based meta-analysis of functional magnetic resonance imaging studies. Eur. Neuropsychopharmacol. 2012;22(2):100–113. doi: 10.1016/j.euroneuro.2011.07.003. [DOI] [PubMed] [Google Scholar]
  52. Delvecchio G., Sugranyes G., Frangou S. Evidence of diagnostic specificity in the neural correlates of facial affect processing in bipolar disorder and schizophrenia: a meta-analysis of functional imaging studies. Psychol. Med. 2013;43(3):553–569. doi: 10.1017/S0033291712001432. [DOI] [PubMed] [Google Scholar]
  53. Derntl B., Seidel E.M., Kryspin-Exner I., Hasmann A., Dobmeier M. Facial emotion recognition in patients with bipolar I and bipolar II disorder. Br. J. Clin. Psychol. 2009;48(Pt 4):363–375. doi: 10.1348/014466509X404845. [DOI] [PubMed] [Google Scholar]
  54. Deveney C.M., Brotman M.A., Thomas L.A., Hinton K.E., Muhrer E.M., Reynolds R.C., Adleman N.E., Zarate C.A., Jr., Pine D.S., Leibenluft E. Neural response during explicit and implicit face processing varies developmentally in bipolar disorder. Soc. Cognit. Affect Neurosci. 2014;9(12):1984–1992. doi: 10.1093/scan/nsu014. [DOI] [PMC free article] [PubMed] [Google Scholar]
  55. Diaz M.T., He G., Gadde S., Bellion C., Belger A., Voyvodic J.T., McCarthy G. The influence of emotional distraction on verbal working memory: an fMRI investigation comparing individuals with schizophrenia and healthy adults. J. Psychiatr. Res. 2011;45(9):1184–1193. doi: 10.1016/j.jpsychires.2011.02.008. [DOI] [PMC free article] [PubMed] [Google Scholar]
  56. Diederen K.M., Neggers S.F., Daalman K., Blom J.D., Goekoop R., Kahn R.S., Sommer I.E. Deactivation of the parahippocampal gyrus preceding auditory hallucinations in schizophrenia. Am. J. Psychiatr. 2010;167(4):427–435. doi: 10.1176/appi.ajp.2009.09040456. [DOI] [PubMed] [Google Scholar]
  57. Dong D., Wang Y., Jia X., Li Y., Chang X., Vandekerckhove M., Luo C., Yao D. Abnormal brain activation during threatening face processing in schizophrenia: a meta-analysis of functional neuroimaging studies. Schizophr. Res. 2018;197:200–208. doi: 10.1016/j.schres.2017.11.013. [DOI] [PubMed] [Google Scholar]
  58. Eickhoff S.B., Laird A.R., Grefkes C., Wang L.E., Zilles K., Fox P.T. Coordinate-based activation likelihood estimation meta-analysis of neuroimaging data: a random-effects approach based on empirical estimates of spatial uncertainty. Hum. Brain Mapp. 2009;30(9):2907–2926. doi: 10.1002/hbm.20718. [DOI] [PMC free article] [PubMed] [Google Scholar]
  59. Eickhoff S.B., Bzdok D., Laird A.R., Roski C., Caspers S., Zilles K., Fox P.T. Co-activation patterns distinguish cortical modules, their connectivity and functional differentiation. Neuroimage. 2011;57(3):938–949. doi: 10.1016/j.neuroimage.2011.05.021. [DOI] [PMC free article] [PubMed] [Google Scholar]
  60. Eickhoff S.B., Bzdok D., Laird A.R., Kurth F., Fox P.T. Activation likelihood estimation meta-analysis revisited. Neuroimage. 2012;59(3):2349–2361. doi: 10.1016/j.neuroimage.2011.09.017. [DOI] [PMC free article] [PubMed] [Google Scholar]
  61. Ellard K.K., Gosai A.K., Felicione J.M., Peters A.T., Shea C.V., Sylvia L.G., Nierenberg A.A., Widge A.S., Dougherty D.D., Deckersbach T. Deficits in frontoparietal activation and anterior insula functional connectivity during regulation of cognitive-affective interference in bipolar disorder. Bipolar Disord. 2019;21(3):244–258. doi: 10.1111/bdi.12709. [DOI] [PMC free article] [PubMed] [Google Scholar]
  62. Ellison-Wright I., Bullmore E. Anatomy of bipolar disorder and schizophrenia: a meta-analysis. Schizophr. Res. 2010;117(1):1–12. doi: 10.1016/j.schres.2009.12.022. [DOI] [PubMed] [Google Scholar]
  63. Evans A.C., Collins D.L., Mills S.R., Brown E.D., Kelly R.L., Peters T.M. 1993 IEEE Conference Record Nuclear Science Symposium and Medical Imaging Conference. IEEE; 1993. 3D statistical neuroanatomical models from 305 MRI volumes; pp. 1813–1817. [Google Scholar]
  64. Filkowski M.M., Haas B.W. Rethinking the use of neutral faces as a baseline in fMRI neuroimaging studies of axis-I psychiatric disorders. J. Neuroimaging. 2017;27(3):281–291. doi: 10.1111/jon.12403. [DOI] [PubMed] [Google Scholar]
  65. Fleck D.E., Kotwal R., Eliassen J.C., Lamy M., Delbello M.P., Adler C.M., Durling M., Cerullo M.A., Strakowski S.M. Preliminary evidence for increased frontosubcortical activation on a motor impulsivity task in mixed episode bipolar disorder. J. Affect. Disord. 2011;133(1–2):333–339. doi: 10.1016/j.jad.2011.03.053. [DOI] [PMC free article] [PubMed] [Google Scholar]
  66. Fortuna A., Ferrante L., Lunardi P. Essential Illustrated Neurosurgery. Springer; Milano: 2001. Brain tumors; pp. 9–58. [DOI] [Google Scholar]
  67. Fox A.S., Shackman A.J. The central extended amygdala in fear and anxiety: closing the gap between mechanistic and neuroimaging research. Neurosci. Lett. 2019;693:58–67. doi: 10.1016/j.neulet.2017.11.056. [DOI] [PMC free article] [PubMed] [Google Scholar]
  68. Frank D.W., Dewitt M., Hudgens-Haney M., Schaeffer D.J., Ball B.H., Schwarz N.F., Hussein A.A., Smart L.M., Sabatinelli D. Emotion regulation: quantitative meta-analysis of functional activation and deactivation. Neurosci. Biobehav. Rev. 2014;45:202–211. doi: 10.1016/j.neubiorev.2014.06.010. [DOI] [PubMed] [Google Scholar]
  69. Freeman D. Suspicious minds: the psychology of persecutory delusions. Clin. Psychol. Rev. 2007;27(4):425–457. doi: 10.1016/j.cpr.2006.10.004. [DOI] [PubMed] [Google Scholar]
  70. Freeman D., Garety P.A. Connecting neurosis and psychosis: the direct influence of emotion on delusions and hallucinations. Behav. Res. Ther. 2003;41(8):923–947. doi: 10.1016/s0005-7967(02)00104-3. [DOI] [PubMed] [Google Scholar]
  71. Fusar-Poli P., Placentino A., Carletti F., Landi P., Allen P., Surguladze S., Benedetti F., Abbamonte M., Gasparotti R., Barale F., Perez J., McGuire P., Politi P. Functional atlas of emotional faces processing: a voxel-based meta-analysis of 105 functional magnetic resonance imaging studies. J. Psychiatry Neurosci. : JPN. 2009;34(6):418–432. [PMC free article] [PubMed] [Google Scholar]
  72. Galeno R., Molina M., Guirao M., Isoardi R. Severity of negative symptoms in schizophrenia correlated to hyperactivity of the left globus pallidus and the right claustrum. A PET study. World J. Biol. Psychiatr. 2004;5(1):20–25. doi: 10.1080/15622970410029903. [DOI] [PubMed] [Google Scholar]
  73. Ganzola R., Maziade M., Duchesne S. Hippocampus and amygdala volumes in children and young adults at high-risk of schizophrenia: research synthesis. Schizophr. Res. 2014;156(1):76–86. doi: 10.1016/j.schres.2014.03.030. [DOI] [PubMed] [Google Scholar]
  74. Goghari V.M., Sponheim S.R. More pronounced deficits in facial emotion recognition for schizophrenia than bipolar disorder. Compr. Psychiatr. 2013;54(4):388–397. doi: 10.1016/j.comppsych.2012.10.012. [DOI] [PMC free article] [PubMed] [Google Scholar]
  75. Gosselin N., Samson S., Adolphs R., Noulhiane M., Roy M., Hasboun D., Baulac M., Peretz I. Emotional responses to unpleasant music correlates with damage to the parahippocampal cortex. Brain : a journal of neurology. 2006;129(Pt 10):2585–2592. doi: 10.1093/brain/awl240. [DOI] [PubMed] [Google Scholar]
  76. Grecucci A., Giorgetta C., Van't Wout M., Bonini N., Sanfey A.G. Reappraising the ultimatum: an fMRI study of emotion regulation and decision making. Cerebral cortex (New York, N.Y. 2013;23(2):399–410. doi: 10.1093/cercor/bhs028. 1991. [DOI] [PubMed] [Google Scholar]
  77. Grecucci A., Sulpizio S., Tommasello E., Vespignani F., Job R. Seeing emotions, reading emotions: behavioral and ERPs evidence of the regulation of pictures and words. PLoS One. 2019;14(5) doi: 10.1371/journal.pone.0209461. [DOI] [PMC free article] [PubMed] [Google Scholar]
  78. Grecucci A., Sığırcı H., Lapomarda G., Amodeo L., Messina I., Frederickson J. Anxiety regulation: from affective neuroscience to clinical practice. Brain Sci. 2020;10(11):846. doi: 10.3390/brainsci10110846. [DOI] [PMC free article] [PubMed] [Google Scholar]
  79. Grecucci A., Messina I., Amodeo L., Lapomarda G., Crescentini C., Dadomo H., Panzeri M., Theuninck A., Frederickson J. A dual route model for regulating emotions: comparing models, techniques and biological mechanisms. Front. Psychol. 2020;11:930. doi: 10.3389/fpsyg.2020.00930. [DOI] [PMC free article] [PubMed] [Google Scholar]
  80. Grecucci A., Neresini A., Job R. In: Giacomoni P., Valentini N., Dellantonio S., editors. vol. 25. Springer; Cham: 2021. The moral algorithm. Toward a neuropsychological model of shame; pp. 217–232. (The Dark Side: Philosophical Reflections on the “Negative Emotions.” Studies in the History of Philosophy of Mind). [DOI] [Google Scholar]
  81. Grotegerd D., Stuhrmann A., Kugel H., Schmidt S., Redlich R., Zwanzger P., Rauch A.V., Heindel W., Zwitserlood P., Arolt V., Suslow T., Dannlowski U. Amygdala excitability to subliminally presented emotional faces distinguishes unipolar and bipolar depression: an fMRI and pattern classification study. Hum. Brain Mapp. 2014;35(7):2995–3007. doi: 10.1002/hbm.22380. [DOI] [PMC free article] [PubMed] [Google Scholar]
  82. Gur R.E., Maany V., Mozley P.D., Swanson C., Bilker W., Gur R.C. Subcortical MRI volumes in neuroleptic-naive and treated patients with schizophrenia. Am. J. Psychiatr. 1998;155(12):1711–1717. doi: 10.1176/ajp.155.12.1711. [DOI] [PubMed] [Google Scholar]
  83. Gurevitch J., Koricheva J., Nakagawa S., Stewart G. Meta-analysis and the science of research synthesis. Nature. 2018;555(7695):175–182. doi: 10.1038/nature25753. [DOI] [PubMed] [Google Scholar]
  84. Haber S.N. The place of dopamine in the cortico-basal ganglia circuit. Neuroscience. 2014;282:248–257. doi: 10.1016/j.neuroscience.2014.10.008. [DOI] [PMC free article] [PubMed] [Google Scholar]
  85. Haber S.N., Knutson B. The reward circuit: linking primate anatomy and human imaging. Neuropsychopharmacology. 2010;35(1):4–26. doi: 10.1038/npp.2009.129. [DOI] [PMC free article] [PubMed] [Google Scholar]
  86. Hägele C., Friedel E., Schlagenhauf F., Sterzer P., Beck A., Bermpohl F., Stoy M., Held-Poschardt D., Wittmann A., Ströhle A., Heinz A. Affective responses across psychiatric disorders-A dimensional approach. Neurosci. Lett. 2016;623:71–78. doi: 10.1016/j.neulet.2016.04.037. [DOI] [PubMed] [Google Scholar]
  87. Haijma S.V., Van Haren N., Cahn W., Koolschijn P.C., Hulshoff Pol H.E., Kahn R.S. Brain volumes in schizophrenia: a meta-analysis in over 18 000 subjects. Schizophr. Bull. 2013;39(5):1129–1138. doi: 10.1093/schbul/sbs118. [DOI] [PMC free article] [PubMed] [Google Scholar]
  88. Hajek T., Alda M., Hajek E., Ivanoff J. Functional neuroanatomy of response inhibition in bipolar disorders--combined voxel based and cognitive performance meta-analysis. J. Psychiatr. Res. 2013;47(12):1955–1966. doi: 10.1016/j.jpsychires.2013.08.015. [DOI] [PubMed] [Google Scholar]
  89. Hall J., Whalley H.C., McKirdy J.W., Romaniuk L., McGonigle D., McIntosh A.M., Baig B.J., Gountouna V.E., Job D.E., Donaldson D.I., Sprengelmeyer R., Young A.W., Johnstone E.C., Lawrie S.M. Overactivation of fear systems to neutral faces in schizophrenia. Biol. Psychiatr. 2008;64(1):70–73. doi: 10.1016/j.biopsych.2007.12.014. [DOI] [PubMed] [Google Scholar]
  90. Herbener E.S. Emotional memory in schizophrenia. Schizophr. Bull. 2008;34(5):875–887. doi: 10.1093/schbul/sbn081. [DOI] [PMC free article] [PubMed] [Google Scholar]
  91. Hirjak D., Wolf R.C., Wilder-Smith E.P., Kubera K.M., Thomann P.A. Motor abnormalities and basal ganglia in schizophrenia: evidence from structural magnetic resonance imaging. Brain Topogr. 2015;28(1):135–152. doi: 10.1007/s10548-014-0377-3. [DOI] [PubMed] [Google Scholar]
  92. Hou J., Song B., Chen A., Sun C., Zhou J., Zhu H., Beauchaine T.P. Review on neural correlates of emotion regulation and music: implications for emotion dysregulation. Front. Psychol. 2017;8:501. doi: 10.3389/fpsyg.2017.00501. [DOI] [PMC free article] [PubMed] [Google Scholar]
  93. Houenou J., Frommberger J., Carde S., Glasbrenner M., Diener C., Leboyer M., Wessa M. Neuroimaging-based markers of bipolar disorder: evidence from two meta-analyses. J. Affect. Disord. 2011;132(3):344–355. doi: 10.1016/j.jad.2011.03.016. [DOI] [PubMed] [Google Scholar]
  94. Howes O.D., Kapur S. The dopamine hypothesis of schizophrenia: version III--the final common pathway. Schizophr. Bull. 2009;35(3):549–562. doi: 10.1093/schbul/sbp006. [DOI] [PMC free article] [PubMed] [Google Scholar]
  95. Hsu D.T., Kirouac G.J., Zubieta J.K., Bhatnagar S. Contributions of the paraventricular thalamic nucleus in the regulation of stress, motivation, and mood. Front. Behav. Neurosci. 2014;8:73. doi: 10.3389/fnbeh.2014.00073. [DOI] [PMC free article] [PubMed] [Google Scholar]
  96. Hulshoff Pol H.E., van Baal G.C., Schnack H.G., Brans R.G., van der Schot A.C., Brouwer R.M., van Haren N.E., Lepage C., Collins D.L., Evans A.C., Boomsma D.I., Nolen W., Kahn R.S. Overlapping and segregating structural brain abnormalities in twins with schizophrenia or bipolar disorder. Arch. Gen. Psychiatr. 2012;69(4):349–359. doi: 10.1001/archgenpsychiatry.2011.1615. [DOI] [PubMed] [Google Scholar]
  97. Hwang J., Lyoo I.K., Dager S.R., Friedman S.D., Oh J.S., Lee J.Y., Kim S.J., Dunner D.L., Renshaw P.F. Basal ganglia shape alterations in bipolar disorder. Am. J. Psychiatr. 2006;163(2):276–285. doi: 10.1176/appi.ajp.163.2.276. [DOI] [PubMed] [Google Scholar]
  98. Jacobson L., Javitt D.C., Lavidor M. Activation of inhibition: diminishing impulsive behavior by direct current stimulation over the inferior frontal gyrus. J. Cognit. Neurosci. 2011;23(11):3380–3387. doi: 10.1162/jocn_a_00020. [DOI] [PubMed] [Google Scholar]
  99. Jogia J., Haldane M., Cobb A., Kumari V., Frangou S. Pilot investigation of the changes in cortical activation during facial affect recognition with lamotrigine monotherapy in bipolar disorder. Br. J. Psychiatry : J. Ment. Sci. 2008;192(3):197–201. doi: 10.1192/bjp.bp.107.037960. [DOI] [PubMed] [Google Scholar]
  100. Johnson J.M., Legesse B., Camprodon J.A., Murray E., Price B.H. The clinical significance of bilateral basal ganglia calcification presenting with mania and delusions. J. Neuropsychiatry Clin. Neurosci. 2013;25(1):68–71. doi: 10.1176/appi.neuropsych.12090222. [DOI] [PubMed] [Google Scholar]
  101. Kanske P., Schönfelder S., Forneck J., Wessa M. Impaired regulation of emotion: neural correlates of reappraisal and distraction in bipolar disorder and unaffected relatives. Transl. Psychiatry. 2015;5(1):e497. doi: 10.1038/tp.2014.137. [DOI] [PMC free article] [PubMed] [Google Scholar]
  102. Kapur S. Psychosis as a state of aberrant salience: a framework linking biology, phenomenology, and pharmacology in schizophrenia. Am. J. Psychiatr. 2003;160(1):13–23. doi: 10.1176/appi.ajp.160.1.13. [DOI] [PubMed] [Google Scholar]
  103. Khafif T.C., Rotenberg L.S., Nascimento C., Beraldi G.H., Lafer B. Emotion regulation in pediatric bipolar disorder: a meta-analysis of published studies. J. Affect. Disord. 2021;285:86–96. doi: 10.1016/j.jad.2021.02.010. [DOI] [PubMed] [Google Scholar]
  104. Knöchel C., Reuter J., Reinke B., Stäblein M., Marbach K., Feddern R., Kuhlmann K., Alves G., Prvulovic D., Wenzler S., Linden D.E., Oertel-Knöchel V. Cortical thinning in bipolar disorder and schizophrenia. Schizophr. Res. 2016;172(1–3):78–85. doi: 10.1016/j.schres.2016.02.007. [DOI] [PubMed] [Google Scholar]
  105. Kohler C.G., Martin E.A. Emotional processing in schizophrenia. Cognit. Neuropsychiatry. 2006;11(3):250–271. doi: 10.1080/13546800500188575. [DOI] [PubMed] [Google Scholar]
  106. Kohn N., Eickhoff S.B., Scheller M., Laird A.R., Fox P.T., Habel U. Neural network of cognitive emotion regulation--an ALE meta-analysis and MACM analysis. Neuroimage. 2014;87:345–355. doi: 10.1016/j.neuroimage.2013.11.001. [DOI] [PMC free article] [PubMed] [Google Scholar]
  107. Koubeissi M.Z., Bartolomei F., Beltagy A., Picard F. Electrical stimulation of a small brain area reversibly disrupts consciousness. Epilepsy Behav. : E&B. 2014;37:32–35. doi: 10.1016/j.yebeh.2014.05.027. [DOI] [PubMed] [Google Scholar]
  108. Krabbendam L., Arts B., van Os J., Aleman A. Cognitive functioning in patients with schizophrenia and bipolar disorder: a quantitative review. Schizophr. Res. 2005;80(2–3):137–149. doi: 10.1016/j.schres.2005.08.004. [DOI] [PubMed] [Google Scholar]
  109. Kravariti E., Dixon T., Frith C., Murray R., McGuire P. Association of symptoms and executive function in schizophrenia and bipolar disorder. Schizophr. Res. 2005;74(2–3):221–231. doi: 10.1016/j.schres.2004.06.008. [DOI] [PubMed] [Google Scholar]
  110. Kurtz M., Mohring P., Förster K., Bauer M., Kanske P. Deficits in explicit emotion regulation in bipolar disorder: a systematic review. Int. J. Behav. Dev. 2021;9(1):15. doi: 10.1186/s40345-021-00221-9. [DOI] [PMC free article] [PubMed] [Google Scholar]
  111. Kuswanto C.N., Sum M.Y., Sim K. Neurocognitive functioning in schizophrenia and bipolar disorder: clarifying concepts of diagnostic dichotomy vs. Continuum. Front. Psychiatr. 2013;4:162. doi: 10.3389/fpsyt.2013.00162. [DOI] [PMC free article] [PubMed] [Google Scholar]
  112. Kuswanto C.N., Chin R., Sum M.Y., Sengupta S., Fagiolini A., McIntyre R.S., Vieta E., Sim K. Shared and divergent neurocognitive impairments in adult patients with schizophrenia and bipolar disorder: whither the evidence? Neurosci. Biobehav. Rev. 2016;61:66–89. doi: 10.1016/j.neubiorev.2015.12.002. [DOI] [PubMed] [Google Scholar]
  113. Laird A.R., Robinson J.L., McMillan K.M., Tordesillas-Gutiérrez D., Moran S.T., Gonzales S.M., Ray K.L., Franklin C., Glahn D.C., Fox P.T., Lancaster J.L. Comparison of the disparity between Talairach and MNI coordinates in functional neuroimaging data: validation of the Lancaster transform. Neuroimage. 2010;51(2):677–683. doi: 10.1016/j.neuroimage.2010.02.048. [DOI] [PMC free article] [PubMed] [Google Scholar]
  114. Lakis N., Jiménez J.A., Mancini-Marïe A., Stip E., Lavoie M.E., Mendrek A. Neural correlates of emotional recognition memory in schizophrenia: effects of valence and arousal. Psychiatr. Res. 2011;194(3):245–256. doi: 10.1016/j.pscychresns.2011.05.010. [DOI] [PubMed] [Google Scholar]
  115. Laloyaux J., De Keyser F., Pinchard A., Della Libera C., Larøi F. Testing a model of auditory hallucinations: the role of negative emotions and cognitive resources. Cognit. Neuropsychiatry. 2019;24(4):256–274. doi: 10.1080/13546805.2019.1629895. [DOI] [PubMed] [Google Scholar]
  116. Lancaster J.L., Tordesillas-Gutiérrez D., Martinez M., Salinas F., Evans A., Zilles K., Mazziotta J.C., Fox P.T. Bias between MNI and Talairach coordinates analyzed using the ICBM-152 brain template. Hum. Brain Mapp. 2007;28(11):1194–1205. doi: 10.1002/hbm.20345. [DOI] [PMC free article] [PubMed] [Google Scholar]
  117. Lang P.J., Bradley M.M., Cuthbert B.N. University of Florida; Gainesville, FL: 2008. International Affective Picture System (IAPS): Affective Ratings of Pictures and Instruction Manual. Technical Report A-8. [Google Scholar]
  118. Lapomarda G., Grecucci A., Messina I., Pappaianni E., Dadomo H. Common and different gray and white matter alterations in bipolar and borderline personality disorder: a source-based morphometry study. Brain Res. 2021;1762 doi: 10.1016/j.brainres.2021.147401. [DOI] [PubMed] [Google Scholar]
  119. Lapomarda G., Pappaianni E., Siugzdaite R., Sanfey A.G., Rumiati R.I., Grecucci A. Out of control: an altered parieto-occipital-cerebellar network for impulsivity in bipolar disorder. Behav. Brain Res. 2021;406 doi: 10.1016/j.bbr.2021.113228. [DOI] [PubMed] [Google Scholar]
  120. Lawrence N.S., Williams A.M., Surguladze S., Giampietro V., Brammer M.J., Andrew C., Frangou S., Ecker C., Phillips M.L. Subcortical and ventral prefrontal cortical neural responses to facial expressions distinguish patients with bipolar disorder and major depression. Biol. Psychiatr. 2004;55(6):578–587. doi: 10.1016/j.biopsych.2003.11.017. [DOI] [PubMed] [Google Scholar]
  121. Lee D.K., Lee H., Park K., Joh E., Kim C.E., Ryu S. Common gray and white matter abnormalities in schizophrenia and bipolar disorder. PLoS One. 2020;15(5) doi: 10.1371/journal.pone.0232826. [DOI] [PMC free article] [PubMed] [Google Scholar]
  122. Lennox B.R., Jacob R., Calder A.J., Lupson V., Bullmore E.T. Behavioural and neurocognitive responses to sad facial affect are attenuated in patients with mania. Psychol. Med. 2004;34(5):795–802. doi: 10.1017/s0033291704002557. [DOI] [PubMed] [Google Scholar]
  123. Lew C., Semendeferi K. In: Evolution of Nervous Systems (Vol. 4, 2nd Ed. Kaas J., Vol, editors. Elsevier; 2017. Evolutionary specializations of the human limbic system; pp. 277–291. [DOI] [Google Scholar]
  124. Li H.J., Chan R.C., McAlonan G.M., Gong Q.Y. Facial emotion processing in schizophrenia: a meta-analysis of functional neuroimaging data. Schizophr. Bull. 2010;36(5):1029–1039. doi: 10.1093/schbul/sbn190. [DOI] [PMC free article] [PubMed] [Google Scholar]
  125. Li H.J., Chan R.C., Gong Q.Y., Liu Y., Liu S.M., Shum D., Ma Z.L. Facial emotion processing in patients with schizophrenia and their non-psychotic siblings: a functional magnetic resonance imaging study. Schizophr. Res. 2012;134(2–3):143–150. doi: 10.1016/j.schres.2011.10.019. [DOI] [PubMed] [Google Scholar]
  126. Li L., Ji E., Tang F., Qiu Y., Han X., Zhang S., Zhang Z., Yang H. Abnormal brain activation during emotion processing of euthymic bipolar patients taking different mood stabilizers. Brain Imaging Behav. 2019;13(4):905–913. doi: 10.1007/s11682-018-9915-z. [DOI] [PubMed] [Google Scholar]
  127. Lichtenstein P., Yip B.H., Björk C., Pawitan Y., Cannon T.D., Sullivan P.F., Hultman C.M. Common genetic determinants of schizophrenia and bipolar disorder in Swedish families: a population-based study. Lancet (London, England) 2009;373(9659):234–239. doi: 10.1016/S0140-6736(09)60072-6. [DOI] [PMC free article] [PubMed] [Google Scholar]
  128. Lindner C., Dannlowski U., Walhöfer K., Rödiger M., Maisch B., Bauer J., Ohrmann P., Lencer R., Zwitserlood P., Kersting A., Heindel W., Arolt V., Kugel H., Suslow T. Social alienation in schizophrenia patients: association with insula responsiveness to facial expressions of disgust. PLoS One. 2014;9(1) doi: 10.1371/journal.pone.0085014. [DOI] [PMC free article] [PubMed] [Google Scholar]
  129. Lindner C., Dannlowski U., Bauer J., Ohrmann P., Lencer R., Zwitserlood P., Kugel H., Suslow T. Affective flattening in patients with schizophrenia: differential association with amygdala response to threat-related facial expression under automatic and controlled processing conditions. Psychiatry Investig. 2016;13(1):102–111. doi: 10.4306/pi.2016.13.1.102. [DOI] [PMC free article] [PubMed] [Google Scholar]
  130. Maat A., van Montfort S.J., de Nijs J., Derks E.M., Kahn R.S., Linszen D.H., van Os J., Wiersma D., Bruggeman R., Cahn W., de Haan L., Krabbendam L., Myin-Germeys I., Group Investigators Emotion processing in schizophrenia is state and trait dependent. Schizophr. Res. 2015;161(2–3):392–398. doi: 10.1016/j.schres.2014.11.027. [DOI] [PubMed] [Google Scholar]
  131. Malhi G.S., Lagopoulos J., Sachdev P.S., Ivanovski B., Shnier R., Ketter T. Is a lack of disgust something to fear? A functional magnetic resonance imaging facial emotion recognition study in euthymic bipolar disorder patients. Bipolar Disord. 2007;9(4):345–357. doi: 10.1111/j.1399-5618.2007.00485.x. [DOI] [PubMed] [Google Scholar]
  132. Mamah D., Wang L., Barch D., de Erausquin G.A., Gado M., Csernansky J.G. Structural analysis of the basal ganglia in schizophrenia. Schizophr. Res. 2007;89(1–3):59–71. doi: 10.1016/j.schres.2006.08.031. [DOI] [PMC free article] [PubMed] [Google Scholar]
  133. Marchand W.R., Lee J.N., Garn C., Thatcher J., Gale P., Kreitschitz S., Johnson S., Wood N. Aberrant emotional processing in posterior cortical midline structures in bipolar II depression. Prog. Neuro-psychopharmacol. Biol. Psychiatry. 2011;35(7):1729–1737. doi: 10.1016/j.pnpbp.2011.05.017. [DOI] [PubMed] [Google Scholar]
  134. McIntosh A.M., Job D.E., Moorhead T.W., Harrison L.K., Forrester K., Lawrie S.M., Johnstone E.C. Voxel-based morphometry of patients with schizophrenia or bipolar disorder and their unaffected relatives. Biol. Psychiatr. 2004;56(8):544–552. doi: 10.1016/j.biopsych.2004.07.020. [DOI] [PubMed] [Google Scholar]
  135. Mendrek A., Bourque J., Dubé A., Lakis N., Champagne J. Emotion processing in women with schizophrenia is menstrual cycle phase and affective valence dependent: an FMRI study. ISRN Psychiatry. 2012 doi: 10.5402/2012/656274. 2012. [DOI] [PMC free article] [PubMed] [Google Scholar]
  136. Messina I., Bianco S., Sambin M., Viviani R. Executive and semantic processes in reappraisal of negative stimuli: insights from a meta-analysis of neuroimaging studies. Front. Psychol. 2015;6:956. doi: 10.3389/fpsyg.2015.00956. [DOI] [PMC free article] [PubMed] [Google Scholar]
  137. Michalopoulou P.G., Surguladze S., Morley L.A., Giampietro V.P., Murray R.M., Shergill S.S. Facial fear processing and psychotic symptoms in schizophrenia: functional magnetic resonance imaging study. Br. J. Psychiatry : J. Ment. Sci. 2008;192(3):191–196. doi: 10.1192/bjp.bp.106.032649. [DOI] [PubMed] [Google Scholar]
  138. Mier D., Lis S., Zygrodnik K., Sauer C., Ulferts J., Gallhofer B., Kirsch P. Evidence for altered amygdala activation in schizophrenia in an adaptive emotion recognition task. Psychiatr. Res. 2014;221(3):195–203. doi: 10.1016/j.pscychresns.2013.12.001. [DOI] [PubMed] [Google Scholar]
  139. Modinos G., Costafreda S.G., van Tol M.J., McGuire P.K., Aleman A., Allen P. Neuroanatomy of auditory verbal hallucinations in schizophrenia: a quantitative meta-analysis of voxel-based morphometry studies. Cortex. 2013;49(4):1046–1055. doi: 10.1016/j.cortex.2012.01.009. [DOI] [PubMed] [Google Scholar]
  140. Möller H.J. Bipolar disorder and schizophrenia: distinct illnesses or a continuum? J. Clin. Psychiatry. 2003;64(Suppl. 6):23–28. [PubMed] [Google Scholar]
  141. Morawetz C., Bode S., Derntl B., Heekeren H.R. The effect of strategies, goals and stimulus material on the neural mechanisms of emotion regulation: a meta-analysis of fMRI studies. Neurosci. Biobehav. Rev. 2017;72:111–128. doi: 10.1016/j.neubiorev.2016.11.014. [DOI] [PubMed] [Google Scholar]
  142. Mullin B.C., Perlman S.B., Versace A., de Almeida J.R., Labarbara E.J., Klein C., Ladouceur C.D., Phillips M.L. An fMRI study of attentional control in the context of emotional distracters in euthymic adults with bipolar disorder. Psychiatr. Res. 2012;201(3):196–205. doi: 10.1016/j.pscychresns.2011.09.002. [DOI] [PMC free article] [PubMed] [Google Scholar]
  143. Ninivaggi F.J. Academic Press, an imprint of Elsevier; London, United Kingdom: 2020. Learned Mindfulness: Physician Engagement and M.D. Wellness. [Google Scholar]
  144. O'Brien K.J., Barch D.M., Kandala S., Karcher N.R. Examining specificity of neural correlates of childhood psychotic-like experiences during an emotional n-back task. Biol. Psychiatry. 2020;5(6):580–590. doi: 10.1016/j.bpsc.2020.02.012. [DOI] [PMC free article] [PubMed] [Google Scholar]
  145. Pappaianni E., De Pisapia N., Siugzdaite R., Crescentini C., Calcagnì A., Job R., Grecucci A. Less is more: morphometric and psychological differences between low and high reappraisers. Cognit. Affect Behav. Neurosci. 2020;20(1):128–140. doi: 10.3758/s13415-019-00757-5. [DOI] [PMC free article] [PubMed] [Google Scholar]
  146. Péron J., Renaud O., Haegelen C., Tamarit L., Milesi V., Houvenaghel J.F., Dondaine T., Vérin M., Sauleau P., Grandjean D. Vocal emotion decoding in the subthalamic nucleus: an intracranial ERP study in Parkinson's disease. Brain Lang. 2017;168:1–11. doi: 10.1016/j.bandl.2016.12.003. [DOI] [PubMed] [Google Scholar]
  147. Pessoa L. A network model of the emotional brain. Trends Cognit. Sci. 2017;21(5):357–371. doi: 10.1016/j.tics.2017.03.002. [DOI] [PMC free article] [PubMed] [Google Scholar]
  148. Piretti L., Pappaianni E., Lunardelli A., Zorzenon I., Ukmar M., Pesavento V., Rumiati R.I., Job R., Grecucci A. The role of amygdala in self-conscious emotions in a patient with acquired bilateral damage. Front. Neurosci. 2020;14:677. doi: 10.3389/fnins.2020.00677. [DOI] [PMC free article] [PubMed] [Google Scholar]
  149. Pozzi E., Vijayakumar N., Rakesh D., Whittle S. Neural correlates of emotion regulation in adolescents and emerging adults: a meta-analytic study. Biol. Psychiatr. 2021;89(2):194–204. doi: 10.1016/j.biopsych.2020.08.006. [DOI] [PubMed] [Google Scholar]
  150. Pradhan B.K., Chakrabarti S., Nehra R., Mankotia A. Cognitive functions in bipolar affective disorder and schizophrenia: comparison. Psychiatr. Clin. Neurosci. 2008;62(5):515–525. doi: 10.1111/j.1440-1819.2008.01844.x. [DOI] [PubMed] [Google Scholar]
  151. Price J.L., Drevets W.C. Neural circuits underlying the pathophysiology of mood disorders. Trends Cognit. Sci. 2012;16(1):61–71. doi: 10.1016/j.tics.2011.12.011. [DOI] [PubMed] [Google Scholar]
  152. Radenbach K., Flaig V., Schneider-Axmann T., Usher J., Reith W., Falkai P., Gruber O., Scherk H. Thalamic volumes in patients with bipolar disorder. Eur. Arch. Psychiatr. Clin. Neurosci. 2010;260(8):601–607. doi: 10.1007/s00406-010-0100-7. [DOI] [PMC free article] [PubMed] [Google Scholar]
  153. Rauch A.V., Reker M., Ohrmann P., Pedersen A., Bauer J., Dannlowski U., Harding L., Koelkebeck K., Konrad C., Kugel H., Arolt V., Heindel W., Suslow T. Increased amygdala activation during automatic processing of facial emotion in schizophrenia. Psychiatr. Res. 2010;182(3):200–206. doi: 10.1016/j.pscychresns.2010.03.005. [DOI] [PubMed] [Google Scholar]
  154. Reske M., Habel U., Kellermann T., Backes V., Jon Shah N., von Wilmsdorff M., Gaebel W., Zilles K., Schneider F. Differential brain activation during facial emotion discrimination in first-episode schizophrenia. J. Psychiatr. Res. 2009;43(6):592–599. doi: 10.1016/j.jpsychires.2008.10.012. [DOI] [PubMed] [Google Scholar]
  155. Roberts D.L., Tatini U., Zimmerman R.S., Bortz J.J., Sirven J.I. Musical hallucinations associated with seizures originating from an intracranial aneurysm. Mayo Clin. Proc. 2001;76(4):423–426. doi: 10.4065/76.4.423. [DOI] [PubMed] [Google Scholar]
  156. Rootes-Murdy K., Glazer K., Mondimore F.M., Goes F.S., Pharmacogenomics of Bipolar Disorder (PGBD) Study. Zandi P.P., Bakker A., DePaulo J.R., Jr., Mahon P.B. A pilot fMRI study of lithium response in bipolar disorder. Psychiatry research. Neuroimaging. 2019;286:1–3. doi: 10.1016/j.pscychresns.2019.02.003. [DOI] [PMC free article] [PubMed] [Google Scholar]
  157. Sabatinelli D., Fortune E.E., Li Q., Siddiqui A., Krafft C., Oliver W.T., Beck S., Jeffries J. Emotional perception: meta-analyses of face and natural scene processing. Neuroimage. 2011;54(3):2524–2533. doi: 10.1016/j.neuroimage.2010.10.011. [DOI] [PubMed] [Google Scholar]
  158. Sagar K.A., Dahlgren M.K., Gönenç A., Gruber S.A. Altered affective processing in bipolar disorder: an fMRI study. J. Affect. Disord. 2013;150(3):1192–1196. doi: 10.1016/j.jad.2013.05.019. [DOI] [PMC free article] [PubMed] [Google Scholar]
  159. Sah P. Fear, anxiety, and the amygdala. Neuron. 2017;96(1):1–2. doi: 10.1016/j.neuron.2017.09.013. [DOI] [PubMed] [Google Scholar]
  160. Sankar A., Purves K., Colic L., Cox Lippard E.T., Millard H., Fan S., Spencer L., Wang F., Pittman B., Constable R.T., Gross J.J., Blumberg H.P. Altered frontal cortex functioning in emotion regulation and hopelessness in bipolar disorder. Bipolar Disord. 2021;23(2):152–164. doi: 10.1111/bdi.12954. [DOI] [PMC free article] [PubMed] [Google Scholar]
  161. Saviola F., Pappaianni E., Monti A., Grecucci A., Jovicich J., De Pisapia N. Trait and state anxiety are mapped differently in the human brain. Sci. Rep. 2020;10(1) doi: 10.1038/s41598-020-68008-z. [DOI] [PMC free article] [PubMed] [Google Scholar]
  162. Schumann C.M., Bauman M.D., Amaral D.G. Abnormal structure or function of the amygdala is a common component of neurodevelopmental disorders. Neuropsychologia. 2011;49(4):745–759. doi: 10.1016/j.neuropsychologia.2010.09.028. [DOI] [PMC free article] [PubMed] [Google Scholar]
  163. Seeman P., Kapur S. Schizophrenia: more dopamine, more D2 receptors. Proc. Natl. Acad. Sci. U.S.A. 2000;97(14):7673–7675. doi: 10.1073/pnas.97.14.7673. [DOI] [PMC free article] [PubMed] [Google Scholar]
  164. Shapleske J., Rossell S.L., Chitnis X.A., Suckling J., Simmons A., Bullmore E.T., Woodruff P.W., David A.S. A computational morphometric MRI study of schizophrenia: effects of hallucinations. Cerebral cortex (New York, N.Y. 2002;12(12):1331–1341. doi: 10.1093/cercor/12.12.1331. 1991. [DOI] [PubMed] [Google Scholar]
  165. Sheffield J.M., Karcher N.R., Barch D.M. Cognitive deficits in psychotic disorders: a lifespan perspective. Neuropsychol. Rev. 2018;28(4):509–533. doi: 10.1007/s11065-018-9388-2. [DOI] [PMC free article] [PubMed] [Google Scholar]
  166. Smith J.B., Lee A.K., Jackson J. The claustrum. Curr. Biol. : CB. 2020;30(23):R1401–R1406. doi: 10.1016/j.cub.2020.09.069. [DOI] [PubMed] [Google Scholar]
  167. Sorella S., Lapomarda G., Messina I., Frederickson J.J., Siugzdaite R., Job R., Grecucci A. Testing the expanded continuum hypothesis of schizophrenia and bipolar disorder. Neural and psychological evidence for shared and distinct mechanisms. NeuroImage. Clinical. 2019;23 doi: 10.1016/j.nicl.2019.101854. [DOI] [PMC free article] [PubMed] [Google Scholar]
  168. Sorella S., Grecucci A., Piretti L., Job R. Do anger perception and the experience of anger share common neural mechanisms? Coordinate-based meta-analytic evidence of similar and different mechanisms from functional neuroimaging studies. Neuroimage. 2021;230 doi: 10.1016/j.neuroimage.2021.117777. [DOI] [PubMed] [Google Scholar]
  169. Stefanopoulou E., Manoharan A., Landau S., Geddes J.R., Goodwin G., Frangou S. Cognitive functioning in patients with affective disorders and schizophrenia: a meta-analysis. Int. Rev. Psychiatr. 2009;21(4):336–356. doi: 10.1080/09540260902962149. [DOI] [PubMed] [Google Scholar]
  170. Stone D.L., Rosopa P.J. The advantages and limitations of using meta-analysis in human resource management research. Hum. Resour. Manag. Rev. 2017;27(1):1–7. doi: 10.1016/j.hrmr.2016.09.001. [DOI] [Google Scholar]
  171. Strakowski S.M. Oxford University Press; New York: 2014. Bipolar Disorder. Oxford American Psychiatry Library. [Google Scholar]
  172. Strakowski S.M., Delbello M.P., Adler C.M. The functional neuroanatomy of bipolar disorder: a review of neuroimaging findings. Mol. Psychiatr. 2005;10(1):105–116. doi: 10.1038/sj.mp.4001585. [DOI] [PubMed] [Google Scholar]
  173. Sulpizio S., Grecucci A., Job R. Tune in to the right frequency: theta changes when distancing from emotions elicited by unpleasant images and words. Eur. J. Neurosci. 2021;53(3):916–928. doi: 10.1111/ejn.15013. [DOI] [PubMed] [Google Scholar]
  174. Surguladze S.A., Marshall N., Schulze K., Hall M.H., Walshe M., Bramon E., Phillips M.L., Murray R.M., McDonald C. Exaggerated neural response to emotional faces in patients with bipolar disorder and their first-degree relatives. Neuroimage. 2010;53(1):58–64. doi: 10.1016/j.neuroimage.2010.05.069. [DOI] [PubMed] [Google Scholar]
  175. Surguladze S.A., Chu E.M., Marshall N., Evans A., Anilkumar A.P., Timehin C., McDonald C., Ecker C., Phillips M.L., David A.S. Emotion processing in schizophrenia: fMRI study of patients treated with risperidone long-acting injections or conventional depot medication. J. Psychopharmacol. 2011;25(6):722–733. doi: 10.1177/0269881110363316. [DOI] [PubMed] [Google Scholar]
  176. Takahashi H., Koeda M., Oda K., Matsuda T., Matsushima E., Matsuura M., Asai K., Okubo Y. An fMRI study of differential neural response to affective pictures in schizophrenia. Neuroimage. 2004;22(3):1247–1254. doi: 10.1016/j.neuroimage.2004.03.028. [DOI] [PubMed] [Google Scholar]
  177. Talairach J., Tournoux P. Thieme Medical Publishers; New York: 1988. Co-planar Stereotaxic Atlas of the Human Brain. [Google Scholar]
  178. Taylor S.F., Kang J., Brege I.S., Tso I.F., Hosanagar A., Johnson T.D. Meta-analysis of functional neuroimaging studies of emotion perception and experience in schizophrenia. Biol. Psychiatr. 2012;71(2):136–145. doi: 10.1016/j.biopsych.2011.09.007. [DOI] [PMC free article] [PubMed] [Google Scholar]
  179. Teng S., Lu C.F., Wang P.S., Li C.T., Tu P.C., Hung C.I., Su T.P., Wu Y.T. Altered resting-state functional connectivity of striatal-thalamic circuit in bipolar disorder. PLoS One. 2014;9(5) doi: 10.1371/journal.pone.0096422. [DOI] [PMC free article] [PubMed] [Google Scholar]
  180. Toda M., Abi-Dargham A. Dopamine hypothesis of schizophrenia: making sense of it all. Curr. Psychiatr. Rep. 2007;9(4):329–336. doi: 10.1007/s11920-007-0041-7. [DOI] [PubMed] [Google Scholar]
  181. Townsend J., Altshuler L.L. Emotion processing and regulation in bipolar disorder: a review. Bipolar Disord. 2012;14(4):326–339. doi: 10.1111/j.1399-5618.2012.01021.x. [DOI] [PubMed] [Google Scholar]
  182. Trémeau F. A review of emotion deficits in schizophrenia. Dialogues Clin. Neurosci. 2006;8(1):59–70. doi: 10.31887/DCNS.2006.8.1/ftremeau. [DOI] [PMC free article] [PubMed] [Google Scholar]
  183. Turkeltaub P.E., Eden G.F., Jones K.M., Zeffiro T.A. Meta-analysis of the functional neuroanatomy of single-word reading: method and validation. Neuroimage. 2002;16(3 Pt 1):765–780. doi: 10.1006/nimg.2002.1131. [DOI] [PubMed] [Google Scholar]
  184. Turkeltaub P.E., Eickhoff S.B., Laird A.R., Fox M., Wiener M., Fox P. Minimizing within-experiment and within-group effects in activation likelihood estimation meta-analyses. Hum. Brain Mapp. 2012;33(1):1–13. doi: 10.1002/hbm.21186. [DOI] [PMC free article] [PubMed] [Google Scholar]
  185. van Erp T.G., Hibar D.P., Rasmussen J.M., Glahn D.C., Pearlson G.D., Andreassen O.A., Agartz I., Westlye L.T., Haukvik U.K., Dale A.M., Melle I., Hartberg C.B., Gruber O., Kraemer B., Zilles D., Donohoe G., Kelly S., McDonald C., Morris D.W., Cannon D.M., et al. Subcortical brain volume abnormalities in 2028 individuals with schizophrenia and 2540 healthy controls via the ENIGMA consortium. Mol. Psychiatr. 2016;21(4):547–553. doi: 10.1038/mp.2015.63. [DOI] [PMC free article] [PubMed] [Google Scholar]
  186. van Lutterveld R., Diederen K.M., Koops S., Begemann M.J., Sommer I.E. The influence of stimulus detection on activation patterns during auditory hallucinations. Schizophr. Res. 2013;145(1–3):27–32. doi: 10.1016/j.schres.2013.01.004. [DOI] [PubMed] [Google Scholar]
  187. Van Snellenberg J.X., de Candia T. Meta-analytic evidence for familial coaggregation of schizophrenia and bipolar disorder. Arch. Gen. Psychiatr. 2009;66(7):748–755. doi: 10.1001/archgenpsychiatry.2009.64. [DOI] [PubMed] [Google Scholar]
  188. Vazza G., Bertolin C., Scudellaro E., Vettori A., Boaretto F., Rampinelli S., De Sanctis G., Perini G., Peruzzi P., Mostacciuolo M.L. Genome-wide scan supports the existence of a susceptibility locus for schizophrenia and bipolar disorder on chromosome 15q26. Mol. Psychiatr. 2007;12(1):87–93. doi: 10.1038/sj.mp.4001895. [DOI] [PubMed] [Google Scholar]
  189. Vytal K., Hamann S. Neuroimaging support for discrete neural correlates of basic emotions: a voxel-based meta-analysis. J. Cognit. Neurosci. 2010;22(12):2864–2885. doi: 10.1162/jocn.2009.21366. [DOI] [PubMed] [Google Scholar]
  190. Walderhaug E., Varga M., San Pedro M., Hu J., Neumeister A. In: Manji H.K., Zarate C.A., editors. vol. 5. Springer; Berlin, Heidelberg: 2011. The role of the aminergic systems in the pathophysiology of bipolar disorder; pp. 107–126. (Behavioral Neurobiology of Bipolar Disorder and its Treatment. Current Topics in Behavioral Neurosciences). [DOI] [PubMed] [Google Scholar]
  191. Waters F., Allen P., Aleman A., Fernyhough C., Woodward T.S., Badcock J.C., Barkus E., Johns L., Varese F., Menon M., Vercammen A., Larøi F. Auditory hallucinations in schizophrenia and nonschizophrenia populations: a review and integrated model of cognitive mechanisms. Schizophr. Bull. 2012;38(4):683–693. doi: 10.1093/schbul/sbs045. [DOI] [PMC free article] [PubMed] [Google Scholar]
  192. Wegbreit E., Cushman G.K., Puzia M.E., Weissman A.B., Kim K.L., Laird A.R., Dickstein D.P. Developmental meta-analyses of the functional neural correlates of bipolar disorder. JAMA Psychiatr. 2014;71(8):926–935. doi: 10.1001/jamapsychiatry.2014.660. [DOI] [PMC free article] [PubMed] [Google Scholar]
  193. Williams L.M., Das P., Harris A.W., Liddell B.B., Brammer M.J., Olivieri G., Skerrett D., Phillips M.L., David A.S., Peduto A., Gordon E. Dysregulation of arousal and amygdala-prefrontal systems in paranoid schizophrenia. Am. J. Psychiatr. 2004;161(3):480–489. doi: 10.1176/appi.ajp.161.3.480. [DOI] [PubMed] [Google Scholar]
  194. Williams L.M., Das P., Liddell B.J., Olivieri G., Peduto A.S., David A.S., Gordon E., Harris A.W. Fronto-limbic and autonomic disjunctions to negative emotion distinguish schizophrenia subtypes. Psychiatr. Res. 2007;155(1):29–44. doi: 10.1016/j.pscychresns.2006.12.018. [DOI] [PubMed] [Google Scholar]
  195. World Health Organization . World Health Organization; 1993. The ICD-10 Classification of Mental and Behavioural Disorders : Diagnostic Criteria for Research.https://apps.who.int/iris/handle/10665/37108 [Google Scholar]
  196. Wright I.C., Rabe-Hesketh S., Woodruff P.W., David A.S., Murray R.M., Bullmore E.T. Meta-analysis of regional brain volumes in schizophrenia. Am. J. Psychiatr. 2000;157(1):16–25. doi: 10.1176/ajp.157.1.16. [DOI] [PubMed] [Google Scholar]
  197. Yalcin-Siedentopf N., Hoertnagl C.M., Biedermann F., Baumgartner S., Deisenhammer E.A., Hausmann A., Kaufmann A., Kemmler G., Mühlbacher M., Rauch A.S., Fleischhacker W.W., Hofer A. Facial affect recognition in symptomatically remitted patients with schizophrenia and bipolar disorder. Schizophr. Res. 2014;152(2–3):440–445. doi: 10.1016/j.schres.2013.11.024. [DOI] [PubMed] [Google Scholar]
  198. Yu K., Cheung C., Leung M., Li Q., Chua S., McAlonan G. Are bipolar disorder and schizophrenia neuroanatomically distinct? An anatomical likelihood meta-analysis. Front. Hum. Neurosci. 2010;4:189. doi: 10.3389/fnhum.2010.00189. [DOI] [PMC free article] [PubMed] [Google Scholar]
  199. Yu T., Jia T., Zhu L., Desrivières S., Macare C., Bi Y., Bokde A., Quinlan E.B., Heinz A., Ittermann B., Liu C., Ji L., Banaschewski T., Ren D., Du L., Hou B., Flor H., Frouin V., Garavan H., Gowland P., et al. Cannabis-associated psychotic-like experiences are mediated by developmental changes in the parahippocampal gyrus. J. Am. Acad. Child Adolesc. Psychiatry. 2020;59(5):642–649. doi: 10.1016/j.jaac.2019.05.034. [DOI] [PubMed] [Google Scholar]
  200. Zhang M., Ma C., Luo Y., Li J., Li Q., Liu Y., Ding C., Qiu J. Neural basis of uncertain cue processing in trait anxiety. Sci. Rep. 2016;6 doi: 10.1038/srep21298. [DOI] [PMC free article] [PubMed] [Google Scholar]
  201. Zhang L., Opmeer E.M., van der Meer L., Aleman A., Ćurčić-Blake B., Ruhé H.G. Altered frontal-amygdala effective connectivity during effortful emotion regulation in bipolar disorder. Bipolar Disord. 2018;20(4):349–358. doi: 10.1111/bdi.12611. [DOI] [PubMed] [Google Scholar]
  202. Zhang B., Wang C., Ma G., Fan H., Wang J., Wang W. Cerebral processing of facial emotions in bipolar I and II disorders: an event-related potential study. J. Affect. Disord. 2018;236:37–44. doi: 10.1016/j.jad.2018.04.098. [DOI] [PubMed] [Google Scholar]
  203. Zhao G., Lau W., Wang C., Yan H., Zhang C., Lin K., Qiu S., Huang R., Zhang R. Schizophrenia Bulletin, Sbab093. Advance online publication; 2021. A comparative multimodal meta-analysis of anisotropy and volume abnormalities in white matter in people suffering from bipolar disorder or schizophrenia. [DOI] [PMC free article] [PubMed] [Google Scholar]

Associated Data

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Supplementary Materials

Multimedia component 1
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Data Availability Statement

Analized data can be found following the link reported in the section "Open practices statement". The MRI coordinates used can be found in the papers reported in Table 1

Articles from Neuroimage: Reports are provided here courtesy of Elsevier

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