The Dark and Gloomy Brain: Grey Matter Volume Alterations in Major Depressive Disorder–Fine-Grained Meta-Analyses

Romeo, Zaira; Biondi, Margherita; Oltedal, Leif; Spironelli, Chiara

doi:https://doi.org/10.1155/2024/6673522

Depression and Anxiety

On this page

Abstract Introduction Methods Results Discussion Data Availability Conflicts of Interest Authors’ Contributions Supplementary Materials References Copyright Related Articles

Review Article | Open Access

Volume 2024 | Article ID 6673522 | https://doi.org/10.1155/2024/6673522

The Dark and Gloomy Brain: Grey Matter Volume Alterations in Major Depressive Disorder–Fine-Grained Meta-Analyses

Zaira Romeo,¹Margherita Biondi,^1,2Leif Oltedal,^3,4and Chiara Spironelli^1,2

Academic Editor: Francesco Craig

Received23 Jun 2023

Revised09 Dec 2023

Accepted16 Feb 2024

Published02 Mar 2024

Abstract

Background. While the brain correlates of major depressive disorder (MDD) have been extensively studied, there is no consensus conclusion so far. Various meta-analyses tried to determine the most consistent findings, but the results are often discordant for grey matter volume (GMV) atrophy and hypertrophy. Applying rigorous and stringent inclusion criteria and controlling for confounding factors, such as the presence of anxiety comorbidity, we carried out two novel meta-analyses on the existing literature to unveil MDD signatures. Methods. A systematic literature search was performed up to January 2023. Seventy-three studies on MDD patients reporting GMV abnormalities were included in the first meta-analysis, for a total of 6167 patients and 6237 healthy controls (HC). To test the effects of anxiety comorbidity, we conducted a second meta-analysis, by adding to the original pure MDD sample a new cohort of MDD patients with comorbid anxiety disorders (308 patients and 342 HC). An activation likelihood estimation (ALE) analysis and a coordinate-based mapping approach separate for atrophy and hypertrophy were used to identify common brain structural alterations among patients. Results. The pure MDD sample exhibited atrophy in the left insula, as well as hypertrophy in the bilateral amygdala and parahippocampal gyri. When we added patients with comorbid anxiety to the original sample, bilateral insula atrophy emerged, whereas the hypertrophy results were not replicated. Conclusions. Our findings revealed important structural alterations in pure MDD patients, particularly in the insula and amygdala, which play key roles in sensory input integration and in emotional processing, respectively. Additionally, the amygdala and parahippocampal gyrus hypertrophy may be related to MDD functional overactivation to emotional stimuli, rumination, and overactive self-referential thinking. Conversely, the presence of anxiety comorbidity revealed separate effects which were not seen in the pure MDD sample, underscoring the importance of strict inclusion criteria for investigations of disorder-specific effects.

1. Introduction

Major depressive disorder (MDD) is the most typical condition among depressive disorders [1], where approximately 280 million individuals are affected worldwide [2]. Due to its pervasiveness and symptom severity, depression is nowadays a leading cause of disability and significantly contributes to the overall global burden of disease [3].

Decades of research have enabled the identification of numerous psychosocial, biological, and genetic correlates of MDD, but its pathophysiology remains unclear [4]. Nevertheless, the application of neuroimaging techniques such as structural magnetic resonance imaging (sMRI) to investigate the neural mechanisms underlying psychiatric diseases [5], together with the advances in automated procedures like voxel-based morphometry (VBM) which allows the objective evaluation of anatomical differences in a whole-brain manner [6], has been providing tangible evidence for the neurobiological correlates of MDD. In particular, grey matter volume (GMV) represents a straightforward and reliable measure for the investigation of brain morphometric differences. Using VBM, even focal and subtle differences can be identified between different populations (mainly patients vs. healthy individuals) [7]. Indeed, notwithstanding the emergence of more complex techniques, both the structural ones focusing on the connectome properties (e.g., [8, 9]) or the white matter fiber integrity (e.g., [10–12]) and those performing multimodal connectivity analyses (e.g., [13, 14]), studying GMV keeps providing foundational knowledge about brain organization and architecture, whereby its comprehension is essential for interpreting more complex structural and functional evidence that may involve multiple components. Moreover, structural aberrations in grey matter are likely to be associated with functional alterations, but the reverse process is more ambiguous: for example, impaired connectivity may occur in brains with normal-appearing GMV, as it is the case of disconnection syndromes [15]. For these reasons, the volumetric study of grey matter is still an informative method to assess brain changes in psychiatric illnesses, and MDD is one of those disorders where GMV investigation, and VBM, in particular, has contributed the most.

Several meta-analyses of whole-brain VBM studies have been published in the last decade, with the aim to summarize the brain structures associated with MDD [16–33]. Overall, these studies show a distribution of GMV atrophy across both cortical and subcortical regions, such as the cingulate [16, 18–21, 23–25, 27] and prefrontal cortices [16, 21, 23, 25, 32], frontal gyrus [18, 19, 21, 23, 24, 27, 29–31, 33], insula [21, 23, 24, 27, 29, 31], hippocampus [18, 21–24, 29, 30, 33], and parahippocampus [16, 19, 21, 29, 30, 33]. Interestingly, alterations in some of these areas have been associated to specific MDD symptoms, highlighting the role of MRI to study the relationship between brain changes and clinical characteristics of this disorder. For example, reduced hippocampal volume has been associated with deficits in memory performance [34] and with increased frequency of episodes or longer illness duration [35], while the cingulate cortex has been shown to be crucial in the regulation of affective states [36]. Moreover, dysfunctions in the insula—a region known for integrating information from limbic and frontal areas, with its anterior division particularly involved in cognitive and affective processes—have been suggested as a correlate for the lack of cognitive inhibition to negative emotions, rumination, and neuropsychological impairments commonly observed in MDD patients [37]. Findings are less consistent in the case of hypertrophy, where only a few whole-brain VBM meta-analyses revealed increased GMV in patients with respect to healthy controls and results were inconsistent across studies. For example, Wise et al. identified greater GMV within the bilateral superior occipital gyrus extending into the cuneus [21], while regions of increased volume were observed in the left visual cortex and the right temporoparietal junction by Sha and Banihashemi [25], or else in the right lingual gyrus of elderly patients as reported by Du et al. [33]. Among studies focusing on first-episode drug-naïve patients, the only convergent result is the GMV increase in the bilateral thalamus, whereas the other findings are still inconsistent [31, 32].

However, it should be pointed out that the above-mentioned results come from heterogeneous studies. Notably, most meta-analyses focused on samples including pharmacologically treated patients [16–26], further subdivided into responders and nonresponders by Liu et al. [27]. Conversely, some investigations considered first-episode cases [28, 29] or medication-free patients [30], whereas others considered only first-episode drug-naïve patients [31, 32]. A meta-analysis focusing only on late-life depression was also published [33]. Moreover, in some instances, broader selection criteria have been used: there are cases in which papers performing small volume correction—a violation of whole-brain analysis because it restricts the search area to a given region of interest—have been included (e.g., [22, 23]), or cases where other factors remained ambiguous, such as the lack of distinction between grey matter density or concentration and GMV (e.g., [18, 26]). Another issue worthy of attention is the presence of clinical samples with a spurious diagnostic profile, for example, by inclusion of remitted patients (e.g., [25]), subjects with a treatment-refractory condition (e.g., [24]), individuals suffering from secondary depression (e.g., [21]), or individuals who manifest subthreshold depressive symptoms (e.g., [19]).

When assessing the comparability of samples from different investigations, we found that patients with overt anxiety comorbidity were often not excluded. Indeed, despite numerous previous meta-analyses declaring a dual psychiatric diagnosis as an exclusion criterion, we still found traces of samples of MDD patients showing comorbid anxiety [17–21, 24, 25, 28, 30–32]. Although the link between anxiety and depression is undoubted [38] with estimated concurrency in about 46% of depressed individuals [39], anxiety and depression are distinct mental disorders, characterized by specific diagnostic criteria [1], and with different clinical and pharmacological treatments. As a consequence, anxiety disorders might have a specific role in affecting brain structures when co-occurring with MDD, and they should be treated like any other comorbid pathologies.

The present study is aimed at studying the brain correlates of MDD in its most characterizing symptomatology. Therefore, we conducted a novel meta-analysis of whole-brain VBM studies on a “pure” MDD sample (i.e., depressed patients without any comorbidities), carefully applying strict inclusion criteria in order to eliminate confounding factors as much as possible. However, anxious symptoms during MDD have been found in 50-60% of cases [40, 41]; hence, our pure MDD subjects were allowed to exhibit anxiety symptoms as long as the criteria for a secondary diagnosis were not fulfilled. Starting from this, we hypothesized that an overt comorbidity between MDD and an anxiety disorder might have a different impact than the simple co-occurrence of anxious symptoms during depressive episodes. This is particularly important considering that anxiety disorders have been associated with both shared and distinct structural alterations when compared with MDD. A comprehensive review of MRI studies revealed, among other brain structures, decreased volumes of the hippocampus, anterior cingulate cortex, insula, and amygdala in patients suffering from anxiety disorders [5]. The authors also highlighted how the variety of the etiopathogenesis among pathological subtypes and the presence of comorbidities can bias the results, preventing generalization [5]. Indeed, when focusing on subtypes of anxiety disorders—separate from MDD—specific alterations have appeared. For example, a recent whole-brain VBM meta-analysis found lower GMV in the thalamus and striatum of patients with social anxiety disorder, but reduced GMV in prefrontal and temporoparietal cortices, thalamus, striatum, and brainstem, together with increased GMV in temporo-parieto-occipital and prefrontal cortices of patients with panic disorder [42]. Therefore, to disclose any potential difference, we conducted a second meta-analysis by adding to the original pure MDD sample a group of MDD patients who also revealed a comorbidity to any of the DSM axis-I anxiety disorders.

The present work represents, to the best of our knowledge, the most complete and updated coordinate-based meta-analysis (CBMA) on MDD. All the studies on the matter published up to 31 January 2023, documenting alterations (both reduction and increase) of GMV at the whole-brain level, have been systematically searched, rigorously screened, and finally analyzed using an activation likelihood estimation (ALE) approach. The final aim is to improve our understanding of the pathophysiology of MDD, with an additional focus on the effects of co-occurrence of anxiety on brain grey matter.

2. Methods

2.1. Inclusion Criteria and Study Selection

A systematic and extensive literature search was carried out in PubMed before the end of January 2023 to identify potential studies according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines [43].

The keywords used for the literature search were as follows: (1) (major depression OR major depressive disorder OR MDD) AND (voxel) AND (morphometry), (2) (major depression OR major depressive disorder OR MDD) AND (structural MRI OR sMRI), and (3) (major depression OR major depressive disorder OR MDD) AND (gray matter volume OR grey matter volume). The procedure returned 2111 records. Additionally, we inspected the complete reference lists of the previous meta-analyses on the matter [16–33]. This final check did not reveal other studies than those obtained with the PubMed search.

Studies were included if they met the following inclusion criteria: (1) they were published in peer-reviewed journals in English, (2) they compared MDD patients with healthy controls (HC), (3) they reported GMV abnormalities using sMRI (studies with no significant results were excluded, as well as studies investigating GM concentration or density), (4) they performed whole-brain VBM analysis (i.e., ROI or small volume correction analyses were not included), (5) they reported stereotactic coordinates in MNI or Talairach space (when the coordinates or the direction of the contrast between patients and HC was not clearly reported, we contacted the corresponding authors; in case of no response, these papers were excluded), (6) they did not investigate any kind of depression different from MDD (e.g., subthreshold depression, secondary depression, peripartum/postpartum depression, psychotic or bipolar depression, dysthymic disorder, or premenstrual dysphoric disorder; we also excluded patients in remission or in euthymic state and those who were treatment-resistant), and (7) they included patients with no overt physical, neurological, or psychic comorbidities. To ensure results belonging to a “pure” MDD sample, we first excluded patients with any comorbid anxiety disorders—but not those showing anxious symptoms. After that, we retrieved the discarded publications to examine the effect produced by adding patients with both disorders to the original sample.

To achieve maximum research coverage and a sample as representative as possible of the clinical population, we did not impose limitations on age (although 95% of the reports included in the final pool analyzed participants whose age was above 18 and below 65) or medication status. We also included studies performing different subgroup comparisons, as long as patient samples were not overlapping. Finally, in the case of longitudinal clinical trials, we considered only the pretreatment baseline (e.g., before patients underwent electroconvulsive therapy) results. The inclusion process is summarized in the flowchart (Figure 1).

2.2. Data Extraction

Three authors visually inspected the “methods” and “results” paragraphs of each article, working independently. At the end of the procedure, the authors compared the results and any discrepancy was resolved by consensus. Next, data were manually extracted from articles that met the inclusion criteria. Data extraction was performed independently, and the resulting file was compared before performing statistical analyses. The results converged.

The final pool of published reports that was included in the meta-analysis on pure MDD consisted of 73 studies, listed in Table 1. From these studies, we extracted data representing a total of 6167 patients and 6237 HC. Notably, the number of healthy individuals here reported was obtained by counting them only once in case of studies performing different patients’ subgroup comparisons with the same HC group (e.g., [44]).

Selected studies reported either decreases or increases in GMV, or both in some cases. In 67 papers, GMV atrophy was reported in patients with respect to HC, and six of them [44, 89, 92, 96, 99, 112] found this result in two separate comparisons between different subgroups of patients versus the same HC group; thus, the total amount of contrasts for GMV atrophy was 73. In the case of GMV hypertrophy in patients compared to HC, 29 papers (23 of which also found atrophy) returned this result; no different subgroup comparisons were performed; thus, the final number of included contrasts was 29.

To test the effects of anxiety comorbidity, we retrieved studies based on samples with dual diagnosis () and an additional subsample affected by anxious comorbidity present in Qi et al. [64] previously discarded (Table 1). Therefore, we conducted a second meta-analysis adding to the original pure MDD sample a new cohort of 308 MDD patients with comorbid anxiety and 342 HC, therefore including six additional contrasts for GMV atrophy () and two for GMV hypertrophy (). Therefore, this overall group, now labelled “MDD+A,” consisted of 6475 patients and 6579 healthy individuals.

2.3. Meta-Analyses

In compliance with the guidelines by Müller et al. [123], statistical analyses were run via GingerALE software version 3.0.2 [124, 125]. We carried out two different meta-analyses, the first one on the pure MDD sample and the second one on the MDD+A sample. Both of them were in turn subdivided into two meta-analyses performed separately, one for GMV atrophy and the other for GMV hypertrophy of patients compared to HC.

To weight study contributions, GingerALE uses sample sizes and coordinates, which must be expressed in the same stereotactic space; therefore, the first step consisted in converting Talairach coordinates into MNI space using the “convert foci” option provided in the GingerALE interface. Then, the activation likelihood estimation (ALE) method [126] was performed under the software to quantitatively assess the interstudy concordance. The ALE approach assesses the spatial convergence of reported coordinates across the experiments against the null hypothesis that the findings follow a random spatial distribution. The coordinates, or foci, are treated as three-dimensional Gaussian probability distributions centered at the given coordinates to generate per-experiment modeled atrophy/hypertrophy maps, which are subsequently joined in a union map [124, 127]. For each Gaussian distribution, the algorithm derives full-width half-maximum by considering the sample size of every single study. Finally, ALE tests for above-chance spatial convergence through a range of available thresholding options. In our meta-analyses, we set a statistical ALE map threshold for significance using cluster-level family-wise error (FWE) correction at (5000 permutations), with cluster-forming threshold of . Forasmuch as each coordinate referred to the contrast between two groups (patients vs. healthy controls), the analysis relied on the of the smaller of the two samples to yield a more conservative activation likelihood estimation [128].

3. Results

3.1. Patients’ Characteristics

Patients included in the pure MDD sample (, 60.2% females) were 34.9 years old on average, the diagnosis of major depression was mostly based on the DSM criteria [1] (the remaining used the ICD [129]), and they had no overt physical, neurological, or psychiatric comorbidities. The severity of depression was assessed with Hamilton Rating Scale for Depression (HAMD) [130] in most cases, but also Beck Depression Inventory (BDI) [131], Montgomery-Asberg Depression Rating Scale (MADRS) [132], and Zung’s Self-Rating Depression Scale (SDS) [133] were sometimes administered. In the 18 included studies, anxious symptomatology—which did not reach the diagnostic threshold for an anxiety disorder diagnosis—was present too and typically measured with Hamilton Anxiety Rating Scale (HAMA) [134]; other scales used were Beck Anxiety Inventory (BAI) [135], Zung’s Self-Rating Anxiety Scale (SAS) [136], and State-Trait Anxiety Inventory (STAI) [137]. During the course of illness, 1222 patients were drug-naïve, 1343 were drug-free (The drug-naïve patients have no prior history of drug treatment -most are first-episode patients- whereas the drug-free patients also include those who received pharmacological treatment in the past but were untreated at the time of the structural MRI acquisition -including, for example, patients who had suspended therapy or were in a “washout” condition), and 2284 were medicated; for the remaining patients, no information about drug status was available. In general, healthy controls were matched to the depressed patients demographically. Indeed, the HC recruited in the comparison studies () were similar to MDD patients regarding age (mean HC ) and sex distribution (57% females).

Patients with comorbid anxiety added in the second meta-analysis (, 62% females) were 36.9 years old on average, and they all met the DSM criteria. Any other comorbidity was excluded. Their depressive symptomatology was mainly assessed with HAMD, while the scales used to evaluate the anxiety symptom severity were HAMA, SAS, and Panic Disorder Severity Scale (PDSS) [138]. The medication status was heterogeneous: in two studies, patients were drug-naïve, in three others drug-free, and in the last two under current treatment. The demographics of healthy subjects () and MDD+A individuals were similar (mean HC , females). See Table 1 for more detailed info.

3.2. Grey Matter Volume Changes in Pure MDD Patients versus Healthy Controls

The first meta-analysis was carried out on 73 contrasts (derived from 67 papers) that included 407 foci and compared 5509 pure MDD patients showing GMV atrophy versus 6618 HC (here, healthy individuals were counted twice in case of contrasts between two different subgroups of patients and the same HC group, as each contrast is taken into account separately). The minimum size for a cluster to be considered statistically significant was 2000 mm³. The minimum cluster size considered significant in the analyses is automatically calculated by the software GingerALE using the thresholding algorithm of cluster-level inference, which simulates random datasets using the characteristics of the original dataset: number of foci, number of foci groups, and subject sizes. By setting a threshold of , GingerALE finds above that threshold the contiguous volumes, “clusters,” and tracks the distribution of their volume. The cluster-level inference-corrected threshold sets the cluster minimum volume such that only, for example, 5% of the simulated data’s clusters exceed this size. In other words, cluster-level inference uses a cluster forming threshold () and the distribution of cluster sizes above the threshold to choose a minimum cluster size. Our results revealed a region of convergence of 3064 mm³ centered in the left insula (MNI coordinates: , , ), with four peaks, three of which were located in the left insula (corresponding to Brodmann area (BA) 13) and one in the left superior temporal gyrus (BA 22). The maximum ALE value (0.0341, ; ) was found within the left insula (MNI coordinates: , , ). Table 2 provides a summary of all significant results. Figure 2 shows that the significant cluster was lateralized in the left hemisphere and that it included the insula and the superior temporal gyrus.

Secondly, we meta-analyzed 29 contrasts (derived from 29 papers), for a total of 63 foci, in which 2188 pure MDD patients had higher GMV than 3072 HC. The minimum cluster size chosen to be statistically significant was 1680 mm³, and two regions converged above this threshold. The first cluster of 1696 mm³ was centered in the left parahippocampal gyrus (MNI coordinates: , , ) with three peaks, all of them belonging to the left parahippocampal gyrus (BA 28/BA 34). The maximum ALE value (0.0121, ; ) was found within the left parahippocampal gyrus (MNI coordinates: , , ). The second cluster of 1688 mm³ was centered in the right parahippocampal gyrus (MNI coordinates: , , ) with three peaks (right BA 28/BA 34 and right amygdala), and the maximum ALE value (0.0156, ; ) was found within the right parahippocampal gyrus (MNI coordinates: , , ). A summary of all significant results is provided in Table 3, whereas Figure 3 shows the significant clusters including the left and right parahippocampal gyri and amygdala.

3.3. Grey Matter Volume Changes in MDD+A Patients versus Healthy Controls

This meta-analysis was conducted on 79 contrasts (derived from 73 papers) including 442 foci and comparing 5797 MDD+A patients showing reduced GMV versus 6960 HC (also here, healthy individuals were counted twice in case of contrasts between two different subgroups of patients and the same HC group, as each contrast is taken into account separately). The minimum size for a cluster to be considered statistically significant was 2304 mm³. The results showed two regions of convergence. The first cluster, 4216 mm³ in size, was centered in the right insula (MNI coordinates: , , ) and had eight peaks distributed among the insula (BA 13), superior temporal gyrus (BA 21), temporal lobe (BA 38), and claustrum, all in the right hemisphere. The maximum ALE value (0.027, ; ) was located in the right insula (MNI coordinates: , , ). The second cluster was 2952 mm³ and centered in the left insula (MNI coordinates: , , ) with four peaks, three of which localized in the left insula (BA 13) and one in the left superior temporal gyrus (BA 22). The maximum ALE value (0.0341, ; ) was found in the left insula (MNI coordinates: , , ). Table 4 lists all significant peaks, and Figure 4 displays the significant clusters.

A second meta-analysis on 2238 MDD+A patients showing higher GMV compared to 3148 HC was conducted, comprising 31 contrasts (derived from 31 papers) for a total of 73 foci, but in this case, no statistically significant clusters were found.

Finally, an exploratory analysis on the six experiments in which patients with comorbid MDD and one overt anxiety disorder showed GMV atrophy when compared with HC has been carried out (see Supplementary materials). We did not consider the cases of hypertrophy due to the even more exiguous number.

4. Discussion

In the present CBMA study, four independent meta-analyses were performed, two of which focused on GMV atrophy and the other two on GMV hypertrophy, with the primary aim of clarifying the specific mechanisms underlying MDD in its purest symptomatologic manifestation and, as a complementary analysis, unveiling the critical role of comorbid anxiety. This last analysis is essential to disclose whether comorbid anxiety has a different impact on MDD brain structures than the simple co-occurrence of anxious symptoms during depressive episodes.

For our main analysis, the rigorous application of strict inclusion criteria permitted us to carry out a greater control of several confounding factors which have often been neglected in previous meta-analyses. Specifically, this includes the investigation of GMD or GMC rather than GMV (it is good practice to keep these values separate [139]) and the inclusion of patients suffering from a form of depression different from MDD, or affected by anxiety comorbidity.

The analysis of our pure MDD sample and similar healthy adults revealed two important findings: depressive patients exhibited both GMV atrophy and hypertrophy.

4.1. MDD Patients’ Atrophy

Our meta-analysis of GMV loss included data from more than 5500 pure MDD patients and revealed an atrophic region centered in the left insula. Although numerous previous whole-brain meta-analyses [16–33] identified GMV atrophy in depressed patients, some of their results were discordant. These inconsistencies might be related to less stringent inclusion criteria, with the consequent presence of spurious samples with anxiety comorbidity. In fact, when we added patients with comorbid anxiety to our pure MDD sample, bilateral insula atrophy emerged. Interestingly, as GingerALE software reveals which studies contribute to the results, we noticed that one-third of the studies contributing to the right insula hub included patients with comorbidities. In other words, although the sample of patients with comorbidities was only 11% as large as the pure MDD sample (288/5509), their contribution was proportionally very large since the right insula hub did not emerge in the pure MDD sample. In contrast, only one of the studies contributing to the left hub belonged to the MDD+A sample. Considering these ratios and the fact that the left insula cluster was the main atrophic result of our pure MDD meta-analysis, we suggest that the left insula might predominately be affected in MDD patients, whereas the right insula might be less specific of MDD and rather associated with comorbid anxiety disorder. Furthermore, the exploratory analysis carried out on the small sample of MDD with anxiety disorder comorbidity revealed significant GMV atrophy in the right parahippocampal/subcallosal gyrus (see Table S1 and Figure S1 in the supplementary materials). This finding should be interpreted with caution, considering the very limited sample size of patient and control groups in the exploratory analysis. However, this analysis also reveals a reduced GMV in a right region, supporting a possible association between atrophy of the right hemisphere circuits and anxiety disorder comorbidity in MDD patients.

Regardless of its hemispheric location, it is important to note that our results converged in finding atrophy in the insula. The insula has wide connections with several brain areas involved in a variety of cognitive processing (e.g., [140, 141]) and has central position in emotional responses [142]. However, the insula also participates in language, sensory-motor, decision-making, salience, and attentional processing (e.g., [143–145]). Furthermore, the insula special function of (external) sensory input integration to the emotional processing in the limbic system supports its integral role for interoception—i.e., the awareness of the internal bodily state. This process represents the mechanism underlying humans’ ability to perceive themselves as something different from the surrounding environment, allowing people to be aware of themselves, and finally to distinguish the image of “self” from “not oneself” [146–148]. In particular, the anterior portion of the insula is critical for processing the emotional component of interoception awareness, mainly depending on its connection with limbic regions [149, 150].

Notably, a recent meta-analysis revealed that important structural dysfunctions in left insula and temporofrontal regions contributed to the severity and persistence of schizophrenia auditory verbal hallucinations [151]. Furthermore, an atypical functional activation of the right insula was found in both hallucinating schizophrenia patients [152] and euthymic bipolar patients [153]. Considering that language lateralization is a crystallized characteristic of the human brain [154], a reduced GMV of left insula could alter the whole network and contribute to the confusion in processing internally and externally generated sensory/emotional experiences, thus possibly explaining several symptoms characterizing the most severe psychiatric disorders (e.g., semantic anomalies, thought disorders, ruminations, and auditory hallucinations).

4.2. MDD Patients’ Hypertrophy

To the best of our knowledge, this is the most complete and updated meta-analysis that compared pure MDD patients showing hypertrophy with respect to healthy controls. We found hypertrophy in the bilateral amygdala and parahippocampal gyri in the pure MDD sample. This result, although still limited, is the newest and most promising one, given the recent and growing interest in structural hypertrophy. Past relevant studies from the ENIGMA MDD Consortium provided evidence of subcortical brain atrophy in a region close to this hub, i.e., the hippocampus [35, 155, 156]. Notably, these analyses were all carried out focusing on specific ROIs, including a small cluster of subcortical brain regions, not following a whole-brain approach. In their recent review, Schmaal et al. found reduced hippocampal volume in MDD patients compared with controls, with a modest effect size [35]. Interestingly, hippocampal atrophy appeared related to long-lasting, persistent, and recurring forms of MDD, characterized by progressive worsening of the disorder, rather than to a premorbid vulnerability factor [35].

The involvement of these areas in major depression has been widely investigated, especially in light of their functional role: the parahippocampal gyrus and the amygdala are part of a circuitry where emotion and memory functions have a mutual modulatory effect [157]. Consistent with this, cognitive theories suggest that memory biases of depressed individuals (e.g., greater access to negative autobiographic material) may interfere with emotion regulation strategies, and vice versa [158]. In particular, the parahippocampal gyrus is a region of the limbic system that has a main role in episodic memory and information processing, with a direct involvement in elaborating visuospatial data [159]. Furthermore, the parahippocampal region is characterized by numerous connections—with frontal, parietal, and temporal areas—which explain its key involvement in high-level cognitive functions, such as the processing of contextual associations [160]. Among its network of connections, the parahippocampus has been also linked to the default mode system [161], whose role in depressive symptoms has received wide consensus [162]. Furthermore, the default mode network with its self-referential nature (i.e., excessive self-inspection and internal and external monitoring) has been suggested as a neural basis of rumination in MDD [163], in the form of repetitive thinking over minor past failings, self-loathing, or guilty preoccupations [1]. Interestingly, Cooney et al. found that the parahippocampal activity was higher in depressed patients than healthy individuals during rumination [164], whereas Zamoscik et al. found that greater connectivity between the parahippocampus and default mode network in MDD patients was related to a sadder mood and more ruminative thoughts [165]. A comprehensive hypothesis suggests that the parahippocampal gyrus might be hyperactivated in depression because of its key role in rumination and autobiographical memory—the highly self-referential part of episodic memory—with a specific focus on negative episodes, thereby leading to the negative-valence emotions typical of MDD [165].

Notably, also the amygdala has been associated with rumination [166] and overactive self-referential thinking [167], functional activities that fit with its main role in affective control [168]. The amygdala has been indeed recognized as a crucial component of the brain circuitry underlying emotion [169], and its specific hyperactivity elicited by negative emotions has long contributed to the investigation of its involvement in depression [170]. For example, numerous functional imaging studies have shown that depressed individuals exhibit exaggerated amygdala reactivity to negative stimuli (e.g., [171–173]) and that, compared to healthy controls, these responses are maintained for a longer period of time [167]. Together with this, the amygdala is a key hub in memory and learning processes, reward mechanisms, and social behavior [174], which are all aspects known to be impaired in MDD [175]. Notably, some previous studies reporting enlarged amygdalar volumes in depressed patients (e.g., [176–178]), comprising also first-episode or recent-onset individuals, mutually agreed in speculating that the enlargement might result from elevated resting metabolism and overactivation in response to emotional stimuli.

Interestingly, it has been shown that treatment-resistant patients often show a functional restoration of both the parahippocampal gyrus and the amygdala when treated with more invasive approaches (e.g., ECT) [179, 180]. We therefore wondered whether our hypertrophy results depend on drug response, but what emerged from a “post hoc quality check” of the GingerALE output is that most of the contributing studies included only drug-free patients. Contrary to what we expected, our results revealed that hypertrophy was not a secondary phenomenon to pharmacological treatment. Then, a highly speculative interpretation of our results is that patients’ hypertrophy represents a biomarker of pure MDD and not of their treatment response. In other words, some subcortical circuits, mainly involved in negative-valence emotional processing, are hyperstimulated by the disease symptoms, thus leading to an increased GMV. In the present study, it was not possible to carry out an in-depth analysis to further distinguish between drug-free patients (i.e., patients who had suspended therapy or were in a washout condition) from drug-naïve patients (i.e., patients with no prior history of drug treatment)—and it was not the main purpose of the present work—because many studies did not clarify this aspect, others defined drug-naïve patients as drug-free, and still others carried out multicenter researches on patients with various drug status and provided limited (when present) information on patients’ pharmacological condition. The possible key role of drugs on hypertrophy results also appears considering the MDD+A meta-analysis, in which only 50 patients with anxiety comorbidity were added. Although the number of comorbidity patients was small, the result obtained for the pure MDD sample was not replicated. As comorbidity patients usually undergo multiple drug treatments, as they are characterized by more severe clinical pictures, a possible interpretation is that the addition of spurious participants may obscure the effects that appear spontaneously in a group of pure ones.

4.3. Final Remarks

In conclusion, increasing the sample size might not necessarily make the analysis statistically more powerful, as it might add a level of variance capable of modifying the results and their interpretation. In our specific case, we suggest that including data that make the pure sample spurious may produce less reliable results. In fact, the presence of an overt comorbidity between MDD and anxiety (unlike the occurrence of anxious symptoms only) in the MDD+A meta-analyses may have produced effects not specifically attributable to major depression.

Our work highlights the importance of using rigorous and stringent inclusion criteria for participant enrollment, in particular considering MDD patients with anxious comorbidity. From a more general perspective, the present study corroborates the importance of relying on neuroimaging methodologies for investigating the neurobiological mechanisms that underlie psychiatric disorders, but also for their clinical applications in diagnosis and treatment. In particular, neuroimaging findings can help to redefine diagnostic boundaries [181] and may provide markers of prognosis and monitor therapies, in an attempt to identify neural system abnormalities characterizing treatment-relevant endophenotypes [182]. Finally, they can provide the rationale for the development of specific neurostimulation approaches [183]. For example, considering our specific results, insula might be proposed—as has already been for other psychiatric conditions [184]—as a possible target for brain stimulation in MDD patients thanks to advancements in technology that allow for noninvasive stimulation of deeper brain areas (such as the insula). As neurostimulation techniques have widely demonstrated their effectiveness for MDD [185] and insular alterations have been consistently found across MDD studies, suggesting their putative key role in depression’s pathophysiology, patients could concretely benefit from an intervention of deep stimulation targeted to this region, within which repeated sessions may induce long-lasting synaptic plasticity. Therefore, future studies might consider investigating insular stimulation as a treatment for MDD.

The present study has some limitations. First, only six studies met all our inclusion criteria for MDD patients with anxiety comorbidities. However, they led to important changes in our results. By adding future studies, it might be possible to disambiguate pure MDD brain correlates from those influenced also by anxiety comorbidity. Second, in our work, we explored the contribution of drug status only qualitatively, mainly because it was not possible to distinguish between drug-free patients and drug-naïve patients. Future studies may clarify this aspect, investigating results depending on pharmacological effects from those specific to MDD. Third, GingerALE software utilizes a coordinate-based approach, and the datasets for atrophy and hypertrophy are considered in separate analyses. The inclusion of studies with no significant results ( out of 35 no results/unsuitable contrasts in Figure 1) is not allowed, preventing us from estimating the risk of publication bias.

Notwithstanding these limiting factors, a clear pattern of left insula atrophy and bilateral amygdala and parahippocampal gyrus hypertrophy in pure depressive patients are noteworthy results deserving attention for future studies that aim at shedding light on anatomical correlates of mental disorders.

Data Availability

The template data collection forms, data extracted from included studies, data used for all analyses, analytic code, and any other materials used in the meta-analyses can be requested to the corresponding author upon justified request for academic purposes only.

Conflicts of Interest

The authors declare that they have no conflicts of interest.

Authors’ Contributions

Zaira Romeo and Margherita Biondi contributed equally to this work.

Supplementary Materials

Grey matter volume atrophy in the subgroup of patients with comorbid MDD and anxiety: an exploratory analysis. We conducted an exploratory analysis on the six experiments in which patients with comorbid MDD and one overt anxiety disorder showed GMV atrophy when compared with HC; we did not consider the cases of hypertrophy due to the even more exiguous number. (Supplementary Materials)

References

American Psychiatric Association, Diagnostic and Statistical Manual of Mental Disorders, American Psychiatric Publishing, Arlington, 5th edition, 2013.
World Health Organization, Depression, 2023, https://www.who.int/news-room/fact-sheets/detail/depression.
GBD 2019 Diseases and Injuries Collaborators, “Global burden of 369 diseases and injuries in 204 countries and territories, 1990–2019: a systematic analysis for the global burden of disease study 2019,” The Lancet, vol. 396, no. 10258, pp. 1204–1222, 2020.
View at: Publisher Site | Google Scholar
C. Otte, S. M. Gold, B. W. Penninx et al., “Major depressive disorder,” Nature Reviews. Disease Primers, vol. 2, no. 16065, 2016.
View at: Publisher Site | Google Scholar
N. Agarwal, J. D. Port, M. Bazzocchi, and P. F. Renshaw, “Update on the use of MR for assessment and diagnosis of psychiatric diseases,” Radiology, vol. 255, no. 1, pp. 23–41, 2010.
View at: Publisher Site | Google Scholar
J. Ashburner and K. J. Friston, “Voxel-based morphometry--the methods,” NeuroImage, vol. 11, no. 6, pp. 805–821, 2000.
View at: Publisher Site | Google Scholar
A. Giorgio and N. De Stefano, “Clinical use of brain volumetry,” Journal of Magnetic Resonance Imaging, vol. 37, no. 1, pp. 1–14, 2013.
View at: Publisher Site | Google Scholar
M. S. Korgaonkar, A. Fornito, L. M. Williams, and S. M. Grieve, “Abnormal structural networks characterize major depressive disorder: a connectome analysis,” Biological Psychiatry, vol. 76, no. 7, pp. 567–574, 2014.
View at: Publisher Site | Google Scholar
J. Cha, J. M. Spielberg, B. Hu, M. Altinay, and A. Anand, “Differences in network properties of the structural connectome in bipolar and unipolar depression,” Psychiatry Research. Neuroimaging, vol. 321, article 111442, 2022.
View at: Publisher Site | Google Scholar
A. F. J. Geraets, S. Köhler, L. W. Vergoossen et al., “The association of white matter connectivity with prevalence, incidence and course of depressive symptoms: the Maastricht study,” Psychological Medicine, vol. 53, no. 12, pp. 5558–5568, 2023.
View at: Publisher Site | Google Scholar
N. A. J. De Witte and S. C. Mueller, “White matter integrity in brain networks relevant to anxiety and depression: evidence from the human connectome project dataset,” Brain Imaging and Behavior, vol. 11, no. 6, pp. 1604–1615, 2017.
View at: Publisher Site | Google Scholar
T. Chen, Z. Chen, and Q. Gong, “White matter-based structural brain network of major depression,” Advances in Experimental Medicine and Biology, vol. 1305, pp. 35–55, 2021.
View at: Publisher Site | Google Scholar
J. Y. Yun and Y. K. Kim, “Graph theory approach for the structural-functional brain connectome of depression,” Progress in Neuro-Psychopharmacology & Biological Psychiatry, vol. 111, p. 110401, 2021.
View at: Publisher Site | Google Scholar
Z. Yao, Y. Zou, W. Zheng et al., “Structural alterations of the brain preceded functional alterations in major depressive disorder patients: evidence from multimodal connectivity,” Journal of Affective Disorders, vol. 253, pp. 107–117, 2019.
View at: Publisher Site | Google Scholar
M. Catani and D. H. Ffytche, “The rises and falls of disconnection syndromes,” Brain: A Journal of Neurology, vol. 128, part 10, pp. 2224–2239, 2005.
View at: Publisher Site | Google Scholar
E. Bora, A. Fornito, C. Pantelis, and M. Yücel, “Gray matter abnormalities in major depressive disorder: a meta-analysis of voxel based morphometry studies,” Journal of Affective Disorders, vol. 138, no. 1–2, pp. 9–18, 2012.
View at: Publisher Site | Google Scholar
J. Sacher, J. Neumann, T. Fünfstück, A. Soliman, A. Villringer, and M. L. Schroeter, “Mapping the depressed brain: a meta-analysis of structural and functional alterations in major depressive disorder,” Journal of Affective Disorders, vol. 140, no. 2, pp. 142–148, 2012.
View at: Publisher Site | Google Scholar
M. Y. Du, Q. Z. Wu, Q. Yue et al., “Voxelwise meta-analysis of gray matter reduction in major depressive disorder,” Progress in Neuro-Psychopharmacology & Biological Psychiatry, vol. 36, no. 1, pp. 11–16, 2012.
View at: Publisher Site | Google Scholar
S. Kühn, M. A. Vanderhasselt, R. De Raedt, and J. Gallinat, “Why ruminators won't stop: the structural and resting state correlates of rumination and its relation to depression,” Journal of Affective Disorders, vol. 141, no. 2-3, pp. 352–360, 2012.
View at: Publisher Site | Google Scholar
C. H. Lai, “Gray matter volume in major depressive disorder: a meta-analysis of voxel-based morphometry studies,” Psychiatry Research, vol. 211, no. 1, pp. 37–46, 2013.
View at: Publisher Site | Google Scholar
T. Wise, J. Radua, E. Via et al., “Common and distinct patterns of grey-matter volume alteration in major depression and bipolar disorder: evidence from voxel-based meta-analysis,” Molecular Psychiatry, vol. 22, no. 10, pp. 1455–1463, 2017.
View at: Publisher Site | Google Scholar
J. P. Gray, V. I. Müller, S. B. Eickhoff, and P. T. Fox, “Multimodal abnormalities of brain structure and function in major depressive disorder: a meta-analysis of neuroimaging studies,” The American Journal of Psychiatry, vol. 177, no. 5, pp. 422–434, 2020.
View at: Publisher Site | Google Scholar
M. Serra-Blasco, J. Radua, C. Soriano-Mas et al., “Structural brain correlates in major depression, anxiety disorders and post-traumatic stress disorder: a voxel-based morphometry meta-analysis,” Neuroscience and Biobehavioral Reviews, vol. 129, pp. 269–281, 2021.
View at: Publisher Site | Google Scholar
L. Zacková, M. Jáni, M. Brázdil, Y. S. Nikolova, and K. Marečková, “Cognitive impairment and depression: meta-analysis of structural magnetic resonance imaging studies,” NeuroImage. Clinical, vol. 32, no. 102830, 2021.
View at: Publisher Site | Google Scholar
Z. Sha and L. Banihashemi, “Integrative omics analysis identifies differential biological pathways that are associated with regional grey matter volume changes in major depressive disorder,” Psychological Medicine, vol. 52, no. 5, pp. 924–935, 2022.
View at: Publisher Site | Google Scholar
F. Brandl, B. Weise, S. Mulej Bratec et al., “Common and specific large-scale brain changes in major depressive disorder, anxiety disorders, and chronic pain: a transdiagnostic multimodal meta-analysis of structural and functional MRI studies,” Neuropsychopharmacology, vol. 47, no. 5, pp. 1071–1080, 2022.
View at: Publisher Site | Google Scholar
J. Liu, X. Xu, Q. Luo et al., “Brain grey matter volume alterations associated with antidepressant response in major depressive disorder,” Scientific Reports, vol. 7, no. 1, article 104642017, 2017.
View at: Publisher Site | Google Scholar
H. Zhang, L. Li, M. Wu et al., “Brain gray matter alterations in first episodes of depression: a meta-analysis of whole-brain studies,” Neuroscience and Biobehavioral Reviews, vol. 60, pp. 43–50, 2016.
View at: Publisher Site | Google Scholar
R. Zheng, Y. Zhang, Z. Yang, S. Han, and J. Cheng, “Reduced brain gray matter volume in patients with first-episode major depressive disorder: a quantitative meta-analysis,” Frontiers in Psychiatry, vol. 12, article 671348, 2021.
View at: Publisher Site | Google Scholar
Y. J. Zhao, M. Y. Du, X. Q. Huang et al., “Brain grey matter abnormalities in medication-free patients with major depressive disorder: a meta-analysis,” Psychological Medicine, vol. 44, no. 14, pp. 2927–2937, 2014.
View at: Publisher Site | Google Scholar
W. Peng, Z. Chen, L. Yin, Z. Jia, and Q. Gong, “Essential brain structural alterations in major depressive disorder: a voxel-wise meta-analysis on first episode, medication-naive patients,” Journal of Affective Disorders, vol. 199, pp. 114–123, 2016.
View at: Publisher Site | Google Scholar
W. Wang, Y. Zhao, X. Hu et al., “Conjoint and dissociated structural and functional abnormalities in first- episode drug-naive patients with major depressive disorder: a multimodal meta- analysis,” Scientific Reports, vol. 7, no. 1, article 10401, 2017.
View at: Publisher Site | Google Scholar
M. Du, J. Liu, Z. Chen et al., “Brain grey matter volume alterations in late-life depression,” Journal of Psychiatry & Neuroscience JPN, vol. 39, no. 6, pp. 397–406, 2014.
View at: Publisher Site | Google Scholar
I. Hickie, S. Naismith, P. B. Ward et al., “Reduced hippocampal volumes and memory loss in patients with early- and late-onset depression,” The British Journal of Psychiatry: the Journal of Mental Science, vol. 186, no. 3, pp. 197–202, 2005.
View at: Publisher Site | Google Scholar
L. Schmaal, E. Pozzi, T. C. Ho et al., “ENIGMA MDD: seven years of global neuroimaging studies of major depression through worldwide data sharing,” Translational Psychiatry, vol. 10, no. 1, pp. 1–19, 2020.
View at: Publisher Site | Google Scholar
M. M. Rive, G. van Rooijen, D. J. Veltman, M. L. Phillips, A. H. Schene, and H. G. Ruhé, “Neural correlates of dysfunctional emotion regulation in major depressive disorder. A systematic review of neuroimaging studies,” Neuroscience and Biobehavioral Reviews, vol. 37, 10, Part 2, pp. 2529–2553, 2013.
View at: Publisher Site | Google Scholar
N. Dusi, G. Delvecchio, C. Rovera, C. A. Altamura, and P. Brambilla, “Voxel-based morphometry imaging studies in major depression,” in Brain Morphometry, G. Spalletta, F. Piras, and T. Gili, Eds., pp. 385–402, Humana Press, New York, NY, 2018.
View at: Publisher Site | Google Scholar
N. H. Kalin, “The critical relationship between anxiety and depression,” The American Journal of Psychiatry, vol. 177, no. 5, pp. 365–367, 2020.
View at: Publisher Site | Google Scholar
R. C. Kessler, N. A. Sampson, P. Berglund et al., “Anxious and non-anxious major depressive disorder in the World Health Organization World Mental Health Surveys,” Epidemiology and Psychiatric Sciences, vol. 24, no. 3, pp. 210–226, 2015.
View at: Google Scholar
M. Fava, J. E. Alpert, C. N. Carmin et al., “Clinical correlates and symptom patterns of anxious depression among patients with major depressive disorder in STARD,” Psychological Medicine, vol. 34, no. 7, pp. 1299–1308, 2004.
View at: Publisher Site | Google Scholar
R. Gaspersz, F. Lamers, J. M. Kent et al., “Longitudinal predictive validity of the DSM-5 anxious distress specifier for clinical outcomes in a large cohort of patients with major depressive disorder,” The Journal of Clinical Psychiatry, vol. 78, no. 2, pp. 207–213, 2017.
View at: Publisher Site | Google Scholar
X. Wang, B. Cheng, S. Wang et al., “Distinct grey matter volume alterations in adult patients with panic disorder and social anxiety disorder: a systematic review and voxel-based morphometry meta-analysis,” Journal of Affective Disorders, vol. 281, pp. 805–823, 2021.
View at: Publisher Site | Google Scholar
M. J. Page, J. E. McKenzie, P. M. Bossuyt et al., “The PRISMA 2020 statement: an updated guideline for reporting systematic reviews,” BMJ, vol. 372, no. 71, 2021.
View at: Publisher Site | Google Scholar
H. Peng, K. Wu, J. Li et al., “Increased suicide attempts in young depressed patients with abnormal temporal-parietal-limbic gray matter volume,” Journal of Affective Disorders, vol. 165, pp. 69–73, 2014.
View at: Publisher Site | Google Scholar
G. Wagner, K. Koch, C. Schachtzabel, J. R. Reichenbach, H. Sauer, and R. G. Schlösser Md, “Enhanced rostral anterior cingulate cortex activation during cognitive control is related to orbitofrontal volume reduction in unipolar depression,” Journal of Psychiatry & Neuroscience: JPN, vol. 33, no. 3, pp. 199–208, 2008.
View at: Google Scholar
K. K. Leung, T. M. Lee, M. M. Wong, L. S. Li, P. S. Yip, and P. L. Khong, “Neural correlates of attention biases of people with major depressive disorder: a voxel-based morphometric study,” Psychological Medicine, vol. 39, no. 7, pp. 1097–1106, 2009.
View at: Publisher Site | Google Scholar
A. K. Mak, M. M. Wong, S. H. Han, and T. M. Lee, “Gray matter reduction associated with emotion regulation in female outpatients with major depressive disorder: a voxel-based morphometry study,” Progress in Neuro-Psychopharmacology & Biological Psychiatry, vol. 33, no. 7, pp. 1184–1190, 2009.
View at: Publisher Site | Google Scholar
K. Zou, W. Deng, T. Li et al., “Changes of brain morphometry in first-episode, drug-naïve, non-late-life adult patients with major depression: an optimized voxel-based morphometry study,” Biological Psychiatry, vol. 67, no. 2, pp. 186–188, 2010.
View at: Publisher Site | Google Scholar
Y. Q. Cheng, J. Xu, P. Chai et al., “Brain volume alteration and the correlations with the clinical characteristics in drug-naïve first-episode MDD patients: a voxel-based morphometry study,” Neuroscience Letters, vol. 480, no. 1, pp. 30–34, 2010.
View at: Publisher Site | Google Scholar
J. P. Hwang, T. W. Lee, S. J. Tsai et al., “Cortical and subcortical abnormalities in late-onset depression with history of suicide attempts investigated with MRI and voxel-based morphometry,” Journal of Geriatric Psychiatry and Neurology, vol. 23, no. 3, pp. 171–184, 2010.
View at: Publisher Site | Google Scholar
J. Scheuerecker, E. M. Meisenzahl, N. Koutsouleris et al., “Orbitofrontal volume reductions during emotion recognition in patients with major depression,” Journal of Psychiatry & Neuroscience: JPN, vol. 35, no. 5, pp. 311–320, 2010.
View at: Publisher Site | Google Scholar
F. Amico, E. Meisenzahl, N. Koutsouleris, M. Reiser, H. J. Möller, and T. Frodl, “Structural MRI correlates for vulnerability and resilience to major depressive disorder,” Journal of Psychiatry & Neuroscience: JPN, vol. 36, no. 1, pp. 15–22, 2011.
View at: Publisher Site | Google Scholar
G. Salvadore, A. C. Nugent, H. Lemaitre et al., “Prefrontal cortical abnormalities in currently depressed versus currently remitted patients with major depressive disorder,” NeuroImage, vol. 54, no. 4, pp. 2643–2651, 2011.
View at: Publisher Site | Google Scholar
C. Ma, J. Ding, J. Li et al., “Resting-state functional connectivity bias of middle temporal gyrus and caudate with altered gray matter volume in major depression,” PloS One, vol. 7, no. 9, article e45263, 2012.
View at: Publisher Site | Google Scholar
L. Wang, W. Dai, Y. Su et al., “Amplitude of low-frequency oscillations in first-episode, treatment-naive patients with major depressive disorder: a resting-state functional MRI study,” PloS One, vol. 7, no. 10, article e48658, 2012.
View at: Publisher Site | Google Scholar
S. M. Grieve, M. S. Korgaonkar, S. H. Koslow, E. Gordon, and L. M. Williams, “Widespread reductions in gray matter volume in depression,” NeuroImage. Clinical, vol. 3, pp. 332–339, 2013.
View at: Publisher Site | Google Scholar
A. Chaney, A. Carballedo, F. Amico et al., “Effect of childhood maltreatment on brain structure in adult patients with major depressive disorder and healthy participants,” Journal of psychiatry & neuroscience: JPN, vol. 39, no. 1, pp. 50–59, 2014.
View at: Publisher Site | Google Scholar
W. Guo, F. Liu, M. Yu et al., “Functional and anatomical brain deficits in drug-naive major depressive disorder,” Progress in Neuro-Psychopharmacology & Biological Psychiatry, vol. 54, pp. 1–6, 2014.
View at: Publisher Site | Google Scholar
J. Jung, J. Kang, E. Won et al., “Impact of lingual gyrus volume on antidepressant response and neurocognitive functions in major depressive disorder: a voxel-based morphometry study,” Journal of Affective Disorders, vol. 169, pp. 179–187, 2014.
View at: Publisher Site | Google Scholar
L. Kong, F. Wu, Y. Tang et al., “Frontal-subcortical volumetric deficits in single episode, medication-naïve depressed patients and the effects of 8 weeks fluoxetine treatment: a VBM-DARTEL study,” PloS One, vol. 9, no. 1, article e79055, 2014.
View at: Publisher Site | Google Scholar
C. H. Lai and Y. T. Wu, “Frontal-insula gray matter deficits in first-episode medication-naïve patients with major depressive disorder,” Journal of Affective Disorders, vol. 160, pp. 74–79, 2014.
View at: Publisher Site | Google Scholar
G. Modinos, P. Allen, M. Frascarelli et al., “Are we really mapping psychosis risk? Neuroanatomical signature of affective disorders in subjects at ultra high risk,” Psychological Medicine, vol. 44, no. 16, pp. 3491–3501, 2014.
View at: Publisher Site | Google Scholar
M. Nakano, K. Matsuo, M. Nakashima et al., “Gray matter volume and rapid decision-making in major depressive disorder,” Progress in Neuro-Psychopharmacology & Biological Psychiatry, vol. 48, pp. 51–56, 2014.
View at: Publisher Site | Google Scholar
H. Qi, Y. Ning, J. Li et al., “Gray matter volume abnormalities in depressive patients with and without anxiety disorders,” Medicine, vol. 93, no. 29, p. e345, 2014.
View at: Publisher Site | Google Scholar
L. Qiu, S. Lui, W. Kuang et al., “Regional increases of cortical thickness in untreated, first-episode major depressive disorder,” Translational Psychiatry, vol. 4, no. 4, p. e378, 2014.
View at: Publisher Site | Google Scholar
Y. Cai, J. Liu, L. Zhang et al., “Grey matter volume abnormalities in patients with bipolar I depressive disorder and unipolar depressive disorder: a voxel-based morphometry study,” Neuroscience Bulletin, vol. 31, no. 1, pp. 4–12, 2015.
View at: Publisher Site | Google Scholar
U. Dannlowski, H. Kugel, D. Grotegerd et al., “NCAN cross-disorder risk variant is associated with limbic gray matter deficits in healthy subjects and major depression,” Neuropsychopharmacology, vol. 40, no. 11, pp. 2510–2516, 2015.
View at: Publisher Site | Google Scholar
J. Fang, N. Mao, X. Jiang, X. Li, B. Wang, and Q. Wang, “Functional and anatomical brain abnormalities and effects of antidepressant in major depressive disorder: combined application of voxel-based morphometry and amplitude of frequency fluctuation in resting state,” Journal of Computer Assisted Tomography, vol. 39, no. 5, pp. 766–773, 2015.
View at: Publisher Site | Google Scholar
C. H. Lai and Y. T. Wu, “The gray matter alterations in major depressive disorder and panic disorder: putative differences in the pathogenesis,” Journal of Affective Disorders, vol. 186, pp. 1–6, 2015.
View at: Publisher Site | Google Scholar
N. Vasic, N. D. Wolf, G. Grön et al., “Baseline brain perfusion and brain structure in patients with major depression: a multimodal magnetic resonance imaging study,” Journal of Psychiatry & Neuroscience: JPN, vol. 40, no. 6, pp. 412–421, 2015.
View at: Publisher Site | Google Scholar
K. Watanabe, S. Kakeda, R. Yoshimura et al., “Relationship between the catechol-O-methyl transferase Val 108/158Met genotype and brain volume in treatment-naive major depressive disorder: voxel-based morphometry analysis,” Psychiatry Research, vol. 233, no. 3, pp. 481–487, 2015.
View at: Publisher Site | Google Scholar
X. Yang, X. Ma, M. Li et al., “Anatomical and functional brain abnormalities in unmedicated major depressive disorder,” Neuropsychiatric Disease and Treatment, vol. 11, pp. 2415–2423, 2015.
View at: Publisher Site | Google Scholar
X. Yang, X. Ma, B. Huang et al., “Gray matter volume abnormalities were associated with sustained attention in unmedicated major depression,” Comprehensive Psychiatry, vol. 63, pp. 71–79, 2015.
View at: Publisher Site | Google Scholar
Z. Chen, W. Peng, H. Sun et al., “High-field magnetic resonance imaging of structural alterations in first- episode, drug-naive patients with major depressive disorder,” Translational Psychiatry, vol. 6, no. 11, article e942, 2016.
View at: Publisher Site | Google Scholar
N. Opel, P. Zwanzger, R. Redlich et al., “Differing brain structural correlates of familial and environmental risk for major depressive disorder revealed by a combined VBM/pattern recognition approach,” Psychological Medicine, vol. 46, no. 2, pp. 277–290, 2016.
View at: Publisher Site | Google Scholar
H. Qiu, X. Li, W. Zhao et al., “Electroconvulsive therapy-induced brain structural and functional changes in major depressive disorders: a longitudinal study,” Medical Science Monitor: International Medical Journal Of Experimental And Clinical Research, vol. 22, pp. 4577–4586, 2016.
View at: Publisher Site | Google Scholar
Z. Shen, Y. Cheng, S. Yang et al., “Changes of grey matter volume in first-episode drug-naive adult major depressive disorder patients with different age-onset,” NeuroImage. Clinical, vol. 12, pp. 492–498, 2016.
View at: Publisher Site | Google Scholar
Y. L. Wang, S. Z. Yang, W. L. Sun, Y. Z. Shi, and H. F. Duan, “Altered functional interaction hub between affective network and cognitive control network in patients with major depressive disorder,” Behavioural Brain Research, vol. 298, no. Part B, pp. 301–309, 2016.
View at: Publisher Site | Google Scholar
N. Igata, S. Kakeda, K. Watanabe et al., “Voxel-based morphometric brain comparison between healthy subjects and major depressive disorder patients in Japanese with the s/s genotype of 5-HTTLPR,” Scientific Reports, vol. 7, no. 1, p. 3931, 2017.
View at: Publisher Site | Google Scholar
X. Yang, Z. Peng, X. Ma et al., “Sex differences in the clinical characteristics and brain gray matter volume alterations in unmedicated patients with major depressive disorder,” Scientific Reports, vol. 7, no. 1, p. 2515, 2017.
View at: Publisher Site | Google Scholar
Y. Zhao, L. Chen, W. Zhang et al., “Gray matter abnormalities in non-comorbid medication-naive patients with major depressive disorder or social anxiety disorder,” eBioMedicine, vol. 21, pp. 228–235, 2017.
View at: Publisher Site | Google Scholar
C. Zhuo, J. Zhu, C. Wang, H. Qu, X. Ma, and W. Qin, “Different spatial patterns of brain atrophy and global functional connectivity impairments in major depressive disorder,” Brain Imaging and Behavior, vol. 11, no. 6, pp. 1678–1689, 2017.
View at: Publisher Site | Google Scholar
M. Chang, F. Y. Womer, E. K. Edmiston et al., “Neurobiological commonalities and distinctions among three major psychiatric diagnostic categories: a structural MRI study,” Schizophrenia Bulletin, vol. 44, no. 1, pp. 65–74, 2018.
View at: Publisher Site | Google Scholar
L. Chen, Y. Wang, C. Niu et al., “Common and distinct abnormal frontal-limbic system structural and functional patterns in patients with major depression and bipolar disorder,” NeuroImage. Clinical, vol. 20, pp. 42–50, 2018.
View at: Publisher Site | Google Scholar
X. W. Lu, H. Guo, J. R. Sun et al., “A shared effect of paroxetine treatment on gray matter volume in depressive patients with and without childhood maltreatment: a voxel-based morphometry study,” CNS Neuroscience & Therapeutics, vol. 24, no. 11, pp. 1073–1083, 2018.
View at: Publisher Site | Google Scholar
D. Zaremba, K. Dohm, R. Redlich et al., “Association of brain cortical changes with relapse in patients with major depressive disorder,” JAMA Psychiatry, vol. 75, no. 5, pp. 484–492, 2018.
View at: Publisher Site | Google Scholar
R. Zhou, F. Wang, G. Zhao et al., “Effects of tumor necrosis factor-α polymorphism on the brain structural changes of the patients with major depressive disorder,” Translational Psychiatry, vol. 8, no. 1, p. 217, 2018.
View at: Publisher Site | Google Scholar
Q. Gong, C. Scarpazza, J. Dai et al., “A transdiagnostic neuroanatomical signature of psychiatric illness,” Neuropsychopharmacology, vol. 44, no. 5, pp. 869–875, 2019.
View at: Publisher Site | Google Scholar
S. C. Hellewell, T. Welton, J. J. Maller et al., “Profound and reproducible patterns of reduced regional gray matter characterize major depressive disorder,” Translational Psychiatry, vol. 9, no. 1, p. 176, 2019.
View at: Publisher Site | Google Scholar
S. Kandilarova, D. Stoyanov, N. Sirakov, M. Maes, and K. Specht, “Reduced grey matter volume in frontal and temporal areas in depression: contributions from voxel-based morphometry study,” Acta Neuropsychiatrica, vol. 31, no. 5, pp. 252–257, 2019.
View at: Publisher Site | Google Scholar
Y. Li, C. Wang, C. Teng et al., “Hippocampus-driving progressive structural alterations in medication-naïve major depressive disorder,” Journal of Affective Disorders, vol. 256, pp. 148–155, 2019.
View at: Publisher Site | Google Scholar
P. Liu, G. Li, A. Zhang et al., “The prognosis and changes of regional brain gray matter volume in MDD with gastrointestinal symptoms,” Neuropsychiatric Disease and Treatment, vol. Volume 15, pp. 1181–1191, 2019.
View at: Publisher Site | Google Scholar
W. Peng, Z. Jia, X. Huang et al., “Brain structural abnormalities in emotional regulation and sensory processing regions associated with anxious depression,” Progress in Neuro-Psychopharmacology & Biological Psychiatry, vol. 94, p. 109676, 2019.
View at: Publisher Site | Google Scholar
J. Straub, R. Brown, K. Malejko et al., “Adolescent depression and brain development: evidence from voxel-based morphometry,” Journal of psychiatry & neuroscience: JPN, vol. 44, no. 4, pp. 237–245, 2019.
View at: Publisher Site | Google Scholar
M. H. Chen, Z. K. Kao, W. C. Chang et al., “Increased proinflammatory cytokines, executive dysfunction, and reduced gray matter volumes in first-episode bipolar disorder and major depressive disorder,” Journal of Affective Disorders, vol. 274, pp. 825–831, 2020.
View at: Publisher Site | Google Scholar
P. Liu, G. Li, A. Zhang et al., “Brain structural and functional alterations in MDD patient with gastrointestinal symptoms: a resting-state MRI study,” Journal of Affective Disorders, vol. 273, pp. 95–105, 2020.
View at: Publisher Site | Google Scholar
Q. Meng, A. Zhang, X. Cao et al., “Brain imaging study on the pathogenesis of depression & therapeutic effect of selective serotonin reuptake inhibitors,” Psychiatry Investigation, vol. 17, no. 7, pp. 688–694, 2020.
View at: Publisher Site | Google Scholar
D. Nan, C. Yuqi, S. Zonglin et al., “Association of a SIRT1 polymorphism with changes of gray matter volume in patients with first-episode medication-naïve major depression,” Psychiatry research. Neuroimaging, vol. 301, p. 111101, 2020.
View at: Publisher Site | Google Scholar
Y. Yang, M. R. Chattun, R. Yan et al., “Atrophy of right inferior frontal orbital gyrus and frontoparietal functional connectivity abnormality in depressed suicide attempters,” Brain Imaging and Behavior, vol. 14, no. 6, pp. 2542–2552, 2020.
View at: Publisher Site | Google Scholar
T. Zhang, B. Zhao, C. Shi et al., “Subthreshold depression may exist on a spectrum with major depressive disorder: evidence from gray matter volume and morphological brain network,” Journal of Affective Disorders, vol. 266, pp. 243–251, 2020.
View at: Publisher Site | Google Scholar
X. Jiang, X. Wang, L. Jia et al., “Structural and functional alterations in untreated patients with major depressive disorder and bipolar disorder experiencing first depressive episode: a magnetic resonance imaging study combined with follow-up,” Journal of Affective Disorders, vol. 279, pp. 324–333, 2021.
View at: Publisher Site | Google Scholar
P. H. Liu, Y. Li, A. X. Zhang et al., “Brain structural alterations in MDD patients with gastrointestinal symptoms: evidence from the REST-meta-MDD project,” Progress in Neuro-Psychopharmacology & Biological Psychiatry, vol. 111, article 110386, 2021.
View at: Publisher Site | Google Scholar
M. Ma, Y. Zhang, X. Zhang, H. Yan, D. Zhang, and W. Yue, “Common and distinct alterations of cognitive function and brain structure in schizophrenia and major depressive disorder: a pilot study,” Frontiers in Psychiatry, vol. 12, p. 705998, 2021.
View at: Publisher Site | Google Scholar
A. Takamiya, T. Vande Casteele, M. Koole et al., “Lower regional gray matter volume in the absence of higher cortical amyloid burden in late-life depression,” Scientific Reports, vol. 11, no. 1, p. 15981, 2021.
View at: Publisher Site | Google Scholar
Y. N. Zhang, H. Li, Z. W. Shen, C. Xu, Y. J. Huang, and R. H. Wu, “Healthy individuals vs patients with bipolar or unipolar depression in gray matter volume,” World Journal of Clinical Cases, vol. 9, no. 6, pp. 1304–1317, 2021.
View at: Publisher Site | Google Scholar
R. Zhou, J. Chen, G. Zhao et al., “Neural biomarker of functional disability in major depressive disorder: a structural neuroimaging study,” Progress in Neuro-Psychopharmacology & Biological Psychiatry, vol. 111, p. 110337, 2021.
View at: Publisher Site | Google Scholar
L. Kang, W. Wang, N. Zhang et al., “Superior temporal gyrus and cerebellar loops predict nonsuicidal self-injury in major depressive disorder patients by multimodal neuroimaging,” Translational Psychiatry, vol. 12, no. 1, p. 474, 2022.
View at: Publisher Site | Google Scholar
X. Li, X. Chen, R. Yu et al., “Changes in gray matter volume following electroconvulsive therapy in adolescent depression with suicidal ideation: a longitudinal structural magnetic resonance imaging study,” Frontiers in Psychiatry, vol. 13, p. 944520, 2022.
View at: Publisher Site | Google Scholar
Y. Liu, J. Zhang, M. Zhang et al., “Abnormal brain gray matter volume in patients with major depressive disorder: associated with childhood trauma?” Journal of Affective Disorders, vol. 308, pp. 562–568, 2022.
View at: Publisher Site | Google Scholar
F. Lu, Q. Cui, Y. Chen et al., “Insular-associated causal network of structural covariance evaluating progressive gray matter changes in major depressive disorder,” Cerebral Cortex, vol. 33, no. 3, pp. 831–843, 2023.
View at: Publisher Site | Google Scholar
S. Lu, C. Wu, L. Jia et al., “Increased plasma levels of IL-6 are associated with striatal structural atrophy in major depressive disorder patients with anhedonia,” Frontiers in Psychiatry, vol. 13, p. 1016735, 2022.
View at: Publisher Site | Google Scholar
N. Sun, M. Liu, P. Liu et al., “Abnormal cortical-striatal-thalamic-cortical circuit centered on the thalamus in MDD patients with somatic symptoms: evidence from the REST-meta-MDD project,” Journal of Affective Disorders, vol. 323, pp. 71–84, 2023.
View at: Publisher Site | Google Scholar
K. Wang, Y. Hu, C. Yan et al., “Brain structural abnormalities in adult major depressive disorder revealed by voxel- and source-based morphometry: evidence from the REST-meta-MDD Consortium,” Psychological Medicine, vol. 53, no. 8, pp. 3672–3682, 2023.
View at: Google Scholar
Y. Yang, X. Li, Y. Cui et al., “Reduced gray matter volume in orbitofrontal cortex across schizophrenia, major depressive disorder, and bipolar disorder: a comparative imaging study,” Frontiers in Neuroscience, vol. 16, p. 919272, 2022.
View at: Publisher Site | Google Scholar
H. Yu, P. Ni, Y. Tian et al., “Association of the plasma complement system with brain volume deficits in bipolar and major depressive disorders,” Psychological Medicine, vol. 53, no. 13, pp. 6102–6112, 2022.
View at: Google Scholar
C. Zhang, P. Ni, S. Liang et al., “Alterations in CRY2 and PER3 gene expression associated with thalamic-limbic community structural abnormalities in patients with bipolar depression or unipolar depression,” Journal of Affective Disorders, vol. 298, no. Part A, pp. 472–480, 2022.
View at: Publisher Site | Google Scholar
C. H. Lai, Y. Y. Hsu, and Y. T. Wu, “First episode drug-naïve major depressive disorder with panic disorder: gray matter deficits in limbic and default network structures,” European Neuropsychopharmacology: The Journal of the European College of Neuropsychopharmacology, vol. 20, no. 10, pp. 676–682, 2010.
View at: Publisher Site | Google Scholar
M. Stratmann, C. Konrad, H. Kugel et al., “Insular and hippocampal gray matter volume reductions in patients with major depressive disorder,” PloS One, vol. 9, no. 7, article e102692, 2014.
View at: Publisher Site | Google Scholar
K. Harada, K. Matsuo, M. Nakashima et al., “Disrupted orbitomedial prefrontal limbic network in individuals with later-life depression,” Journal of Affective Disorders, vol. 204, pp. 112–119, 2016.
View at: Publisher Site | Google Scholar
J. Yang, Y. Yin, C. Svob et al., “Amygdala atrophy and its functional disconnection with the cortico-striatal-pallidal-thalamic circuit in major depressive disorder in females,” PloS One, vol. 12, no. 1, article e0168239, 2017.
View at: Publisher Site | Google Scholar
S. Lu, R. Xu, J. Cao et al., “The left dorsolateral prefrontal cortex volume is reduced in adults reporting childhood trauma independent of depression diagnosis,” Journal of Psychiatric Research, vol. 112, pp. 12–17, 2019.
View at: Publisher Site | Google Scholar
Y. Zhang, Y. Yang, L. Zhu et al., “Volumetric deficit within the fronto-limbic-striatal circuit in first-episode drug naïve patients with major depression disorder,” Frontiers in Psychiatry, vol. 11, article 600583, 2021.
View at: Publisher Site | Google Scholar
V. I. Müller, E. C. Cieslik, A. R. Laird et al., “Ten simple rules for neuroimaging meta-analysis,” Neuroscience and Biobehavioral Reviews, vol. 84, pp. 151–161, 2018.
View at: Publisher Site | Google Scholar
S. B. Eickhoff, A. R. Laird, C. Grefkes, L. E. Wang, K. Zilles, and P. T. Fox, “Coordinate-based activation likelihood estimation meta-analysis of neuroimaging data: a random-effects approach based on empirical estimates of spatial uncertainty,” Human Brain Mapping, vol. 30, no. 9, pp. 2907–2926, 2009.
View at: Publisher Site | Google Scholar
S. B. Eickhoff, D. Bzdok, A. R. Laird, F. Kurth, and P. T. Fox, “Activation likelihood estimation meta-analysis revisited,” NeuroImage, vol. 59, no. 3, pp. 2349–2361, 2012.
View at: Publisher Site | Google Scholar
P. E. Turkeltaub, S. B. Eickhoff, A. R. Laird, M. Fox, M. Wiener, and P. Fox, “Minimizing within-experiment and within-group effects in activation likelihood estimation meta-analyses,” Human Brain Mapping, vol. 33, no. 1, pp. 1–13, 2012.
View at: Publisher Site | Google Scholar
A. R. Laird, P. M. Fox, C. J. Price et al., “ALE meta-analysis: controlling the false discovery rate and performing statistical contrasts,” Human Brain Mapping, vol. 25, no. 1, pp. 155–164, 2005.
View at: Publisher Site | Google Scholar
I. S. Ramsay, “An activation likelihood estimate meta-analysis of thalamocortical dysconnectivity in psychosis,” Biological Psychiatry: Cognitive Neuroscience and Neuroimaging, vol. 4, no. 10, pp. 859–869, 2019.
View at: Publisher Site | Google Scholar
World Health Organization, International statistical classification of diseases and related health problems, World Health Organization, 11th edition, 2019.
M. Hamilton, “A rating scale for depression,” Journal of Neurology, Neurosurgery, and Psychiatry, vol. 23, no. 1, pp. 56–62, 1960.
View at: Publisher Site | Google Scholar
A. T. Beck, C. H. Ward, M. Mendelson, J. Mock, and J. Erbaugh, “An inventory for measuring depression,” Archives of General Psychiatry, vol. 4, no. 6, pp. 561–571, 1961.
View at: Publisher Site | Google Scholar
S. A. Montgomery and M. Asberg, “A new depression scale designed to be sensitive to change,” The British Journal of Psychiatry: the Journal of Mental Science, vol. 134, no. 4, pp. 382–389, 1979.
View at: Publisher Site | Google Scholar
W. W. K. Zung, “A self-rating depression scale,” Archives of General Psychiatry, vol. 12, no. 1, pp. 63–70, 1965.
View at: Publisher Site | Google Scholar
M. Hamilton, “The assessment of anxiety states by rating,” The British Journal of Medical Psychology, vol. 32, no. 1, pp. 50–55, 1959.
View at: Publisher Site | Google Scholar
A. T. Beck, N. Epstein, G. Brown, and R. A. Steer, “An inventory for measuring clinical anxiety: psychometric properties,” Journal of Consulting and Clinical Psychology, vol. 56, no. 6, pp. 893–897, 1988.
View at: Publisher Site | Google Scholar
W. W. Zung, “A rating instrument for anxiety disorders,” Psychosomatics, vol. 12, no. 6, pp. 371–379, 1971.
View at: Publisher Site | Google Scholar
C. D. Spielberger, L. Gorsuch, L. Laux, P. Glanzmann, and P. Schaffner, Das State-Trait-Angstinventar: STAI, Beltz Test, Göttingen, 2001.
M. K. Shear, T. A. Brown, D. H. Barlow et al., “Multicenter collaborative panic disorder severity scale,” American Journal of Psychiatry, vol. 154, no. 11, pp. 1571–1575, 1997.
View at: Publisher Site | Google Scholar
A. Mechelli, C. J. Price, K. J. Friston, and J. Ashburner, “Voxel-based morphometry of the human brain: methods and applications,” Current Medical Imaging Reviews, vol. 1, no. 2, pp. 105–113, 2005.
View at: Publisher Site | Google Scholar
U. Türe, D. C. H. Yaşargil, O. Al-Mefty, and M. G. Yaşargil, “Topographic anatomy of the insular region,” Journal of Neurosurgery, vol. 90, no. 4, pp. 720–733, 1999.
View at: Publisher Site | Google Scholar
K. P. Wylie and J. R. Tregellas, “The role of the insula in schizophrenia,” Schizophrenia Research, vol. 123, no. 2–3, pp. 93–104, 2010.
View at: Publisher Site | Google Scholar
P. G. Gasquoine, “Contributions of the insula to cognition and emotion,” Neuropsychology Review, vol. 24, no. 2, pp. 77–87, 2014.
View at: Publisher Site | Google Scholar
D. E. Bamiou, F. E. Musiek, and L. M. Luxon, “The insula (Island of Reil) and its role in auditory processing: literature review,” Brain Research Reviews, vol. 42, no. 2, pp. 143–154, 2003.
View at: Publisher Site | Google Scholar
A. Oh, E. G. Duerden, and E. W. Pang, “The role of the insula in speech and language processing,” Brain and Language, vol. 135, pp. 96–103, 2014.
View at: Publisher Site | Google Scholar
L. Q. Uddin, J. S. Nomi, B. Hebert-Seropian, J. Ghaziri, and O. Boucher, “Structure and function of the human insula,” Journal of Clinical Neurophysiology, vol. 34, no. 4, pp. 300–306, 2017.
View at: Publisher Site | Google Scholar
A. Damasio, “Mental self: The person within,” Nature, vol. 423, no. 6937, p. 227, 2003.
View at: Publisher Site | Google Scholar
C. Devue, F. Collette, E. Balteau et al., “Here I am: the cortical correlates of visual self-recognition,” Brain Research, vol. 1143, no. 1, pp. 169–182, 2007.
View at: Publisher Site | Google Scholar
T. T. J. Kircher, C. Senior, M. L. Phillips et al., “Recognizing one’s own face,” Cognition, vol. 78, no. 1, pp. B1–15, 2001.
View at: Publisher Site | Google Scholar
A. D. Craig, “Interoception: the sense of the physiological condition of the body,” Current Opinion in Neurobiology, vol. 13, no. 4, pp. 500–505, 2003.
View at: Publisher Site | Google Scholar
H. D. Critchley, R. N. Melmed, E. Featherstone, C. J. Mathias, and R. J. Dolan, “Volitional control of autonomic arousal: a functional magnetic resonance study,” NeuroImage, vol. 16, no. 4, pp. 909–919, 2002.
View at: Publisher Site | Google Scholar
Z. Romeo and C. Spironelli, “Hearing voices in the head: two meta-analyses on structural correlates of auditory hallucinations in schizophrenia,” NeuroImage Clinical, vol. 36, article 103241, 2022.
View at: Publisher Site | Google Scholar
C. Spironelli, M. Marino, D. Mantini et al., “fMRI fluctuations within the language network are correlated with severity of hallucinatory symptoms in schizophrenia,” Schizophrenia (Heidelberg, Germany), vol. 9, no. 1, p. 75, 2023.
View at: Publisher Site | Google Scholar
Z. Romeo, M. Marino, A. Angrilli et al., “Altered language network lateralization in euthymic bipolar patients: a pilot study,” Translational Psychiatry, vol. 12, no. 1, p. 435, 2022.
View at: Publisher Site | Google Scholar
C. Spironelli and A. Angrilli, “Brain plasticity in aphasic patients: intra- and inter-hemispheric reorganisation of the whole linguistic network probed by N150 and N350 components,” Scientific Reports, vol. 5, no. 1, article 12541, 2015.
View at: Publisher Site | Google Scholar
L. Schmaal, D. J. Veltman, T. G. van Erp et al., “Subcortical brain alterations in major depressive disorder: findings from the ENIGMA major depressive disorder working group,” Molecular Psychiatry, vol. 21, no. 6, pp. 806–812, 2016.
View at: Publisher Site | Google Scholar
L. Schmaal, D. P. Hibar, P. G. Sämann et al., “Cortical abnormalities in adults and adolescents with major depression based on brain scans from 20 cohorts worldwide in the ENIGMA major depressive disorder working Group,” Molecular Psychiatry, vol. 22, no. 6, pp. 900–909, 2017.
View at: Publisher Site | Google Scholar
M. G. Packard and B. J. Knowlton, “Learning and memory functions of the basal ganglia,” Annual Review of Neuroscience, vol. 25, no. 1, pp. 563–593, 2002.
View at: Publisher Site | Google Scholar
J. Joormann and M. E. Quinn, “Cognitive processes and emotion regulation in depression,” Depression and Anxiety, vol. 31, no. 4, pp. 308–315, 2014.
View at: Publisher Site | Google Scholar
F. D. Raslau, I. T. Mark, A. P. Klein, J. L. Ulmer, V. Mathews, and L. P. Mark, “Memory part 2: the role of the medial temporal lobe,” American Journal of Neuroradiology, vol. 36, no. 5, pp. 846–849, 2015.
View at: Publisher Site | Google Scholar
E. M. Aminoff, K. Kveraga, and M. Bar, “The role of the parahippocampal cortex in cognition,” Trends in Cognitive Sciences, vol. 17, no. 8, pp. 379–390, 2013.
View at: Publisher Site | Google Scholar
J. L. Price and W. C. Drevets, “Neurocircuitry of mood disorders,” Neuropsychopharmacology, vol. 35, no. 1, pp. 192–216, 2010.
View at: Publisher Site | Google Scholar
Y. I. Sheline, D. M. Barch, J. L. Price et al., “The default mode network and self-referential processes in depression,” Proceedings of the National Academy of Sciences of the United States of America, vol. 106, no. 6, pp. 1942–1947, 2009.
View at: Publisher Site | Google Scholar
J. P. Hamilton, M. Farmer, P. Fogelman, and I. H. Gotlib, “Depressive rumination, the default-mode network, and the dark matter of clinical neuroscience,” Biological Psychiatry, vol. 78, no. 4, pp. 224–230, 2015.
View at: Publisher Site | Google Scholar
R. E. Cooney, J. Joormann, F. Eugène, E. L. Dennis, and I. H. Gotlib, “Neural correlates of rumination in depression,” Cognitive, Affective, & Behavioral Neuroscience, vol. 10, no. 4, pp. 470–478, 2010.
View at: Publisher Site | Google Scholar
V. Zamoscik, S. Huffziger, U. Ebner-Priemer, C. Kuehner, and P. Kirsch, “Increased involvement of the parahippocampal gyri in a sad mood predicts future depressive symptoms,” Social Cognitive and Affective Neuroscience, vol. 9, no. 12, pp. 2034–2040, 2014.
View at: Publisher Site | Google Scholar
R. D. Ray, K. N. Ochsner, J. C. Cooper, E. R. Robertson, J. D. E. Gabrieli, and J. J. Gross, “Individual differences in trait rumination and the neural systems supporting cognitive reappraisal,” Cognitive, Affective, & Behavioral Neuroscience, vol. 5, no. 2, pp. 156–168, 2005.
View at: Publisher Site | Google Scholar
G. J. Siegle, S. R. Steinhauer, M. E. Thase, A. Stenger, and C. S. Carter, “Can't shake that feeling: event-related fMRI assessment of sustained amygdala activity in response to emotional information in depressed individuals,” Biological Psychiatry, vol. 51, no. 9, pp. 693–707, 2002.
View at: Google Scholar
J. E. LeDoux, “Emotion: clues from the brain,” Annual Review of Psychology, vol. 46, no. 1, pp. 209–235, 1995.
View at: Publisher Site | Google Scholar
M. Gallagher and A. A. Chiba, “The amygdala and emotion,” Current Opinion in Neurobiology, vol. 6, no. 2, pp. 221–227, 1996.
View at: Publisher Site | Google Scholar
M. L. Phillips, W. C. Drevets, S. L. Rauch, and R. Lane, “Neurobiology of emotion perception II: implications for major psychiatric disorders,” Biological Psychiatry, vol. 54, no. 5, pp. 515–528, 2003.
View at: Publisher Site | Google Scholar
Y. I. Sheline, D. M. Barch, J. M. Donnelly, J. M. Ollinger, A. Z. Snyder, and M. A. Mintun, “Increased amygdala response to masked emotional faces in depressed subjects resolves with antidepressant treatment: an fMRI study,” Biological Psychiatry, vol. 50, no. 9, pp. 651–658, 2001.
View at: Publisher Site | Google Scholar
T. Suslow, C. Konrad, H. Kugel et al., “Automatic mood-congruent amygdala responses to masked facial expressions in major depression,” Biological Psychiatry, vol. 67, no. 2, pp. 155–160, 2010.
View at: Publisher Site | Google Scholar
T. A. Victor, M. L. Furey, S. J. Fromm, A. Ohman, and W. C. Drevets, “Relationship between amygdala responses to masked faces and mood state and treatment in major depressive disorder,” Archives of General Psychiatry, vol. 67, no. 11, pp. 1128–1138, 2010.
View at: Publisher Site | Google Scholar
M. G. Baxter and E. A. Murray, “The amygdala and reward,” Nature Reviews. Neuroscience, vol. 3, no. 7, pp. 563–573, 2002.
View at: Publisher Site | Google Scholar
W. C. Drevets, “Neuroimaging and neuropathological studies of depression: implications for the cognitive-emotional features of mood disorders,” Current Opinion in Neurobiology, vol. 11, no. 2, pp. 240–249, 2001.
View at: Publisher Site | Google Scholar
C. Lange and E. Irle, “Enlarged amygdala volume and reduced hippocampal volume in young women with major depression,” Psychological Medicine, vol. 34, no. 6, pp. 1059–1064, 2004.
View at: Publisher Site | Google Scholar
G. Weniger, C. Lange, and E. Irle, “Abnormal size of the amygdala predicts impaired emotional memory in major depressive disorder,” Journal of Affective Disorders, vol. 94, no. 1-3, pp. 219–229, 2006.
View at: Publisher Site | Google Scholar
T. Frodl, E. M. Meisenzahl, T. Zetzsche et al., “Larger amygdala volumes in first depressive episode as compared to recurrent major depression and healthy control subjects,” Biological Psychiatry, vol. 53, no. 4, pp. 338–344, 2003.
View at: Publisher Site | Google Scholar
G. E. Brancati, N. Brekke, H. Bartsch et al., “Short and long-term effects of single and multiple sessions of electroconvulsive therapy on brain gray matter volumes,” Brain Stimulation, vol. 14, no. 5, pp. 1330–1339, 2021.
View at: Publisher Site | Google Scholar
K. Gbyl and P. Videbech, “Electroconvulsive therapy increases brain volume in major depression: a systematic review and meta-analysis,” Acta Psychiatrica Scandinavica, vol. 138, no. 3, pp. 180–195, 2018.
View at: Publisher Site | Google Scholar
D. E. J. Linden, “The challenges and promise of neuroimaging in psychiatry,” Neuron, vol. 73, no. 1, pp. 8–22, 2012.
View at: Publisher Site | Google Scholar
M. L. Phillips, “The emerging role of neuroimaging in psychiatry: characterizing treatment-relevant endophenotypes,” American Journal of Psychiatry, vol. 164, no. 5, pp. 697–699, 2007.
View at: Publisher Site | Google Scholar
D. A. Silbersweig and S. L. Rauch, “Neuroimaging in psychiatry: a quarter century of progress,” Harvard Review of Psychiatry, vol. 25, no. 5, pp. 195–197, 2017.
View at: Publisher Site | Google Scholar
C. Ibrahim, D. S. Rubin-Kahana, A. Pushparaj et al., “The insula: a brain stimulation target for the treatment of addiction,” Frontiers in Pharmacology, vol. 10, no. 720, 2019.
View at: Publisher Site | Google Scholar
H. Akhtar, F. Bukhari, M. Nazir, M. N. Anwar, and A. Shahzad, “Therapeutic efficacy of neurostimulation for depression: techniques, current modalities, and future challenges,” Neuroscience Bulletin, vol. 32, no. 1, pp. 115–126, 2016.
View at: Publisher Site | Google Scholar

Copyright

Copyright © 2024 Zaira Romeo et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

PDF Download Citation

Download other formats

Order printed copies

Views

320

Downloads

147

Citations