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Article ; Online: Targeting Developmental Thalamocortical Connectivity Abnormalities for Psychosis Prediction: How Far Are We From Biomarker Identification?

Maggioni, Eleonora / Brambilla, Paolo

Biological psychiatry. Cognitive neuroscience and neuroimaging

2022 Volume 7, Issue 8, Page(s) 749–751

MeSH term(s)	Biomarkers ; Humans ; Psychotic Disorders/diagnosis ; Thalamus
Chemical Substances	Biomarkers
Language	English
Publishing date	2022-08-08
Publishing country	United States
Document type	Journal Article ; Research Support, Non-U.S. Gov't ; Comment
ZDB-ID	2879089-3
ISSN	2451-9030 ; 2451-9022
ISSN (online)	2451-9030
ISSN	2451-9022
DOI	10.1016/j.bpsc.2022.06.008
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Article ; Online: A review of resting-state fMRI correlations with executive functions and social cognition in bipolar disorder.

Massalha, Yara / Maggioni, Eleonora / Callari, Antonio / Brambilla, Paolo / Delvecchio, Giuseppe

Journal of affective disorders

2023 Volume 334, Page(s) 337–351

Abstract: Background: Deficits in executive functions (EF) and social cognition (SC) are often observed in bipolar disorder (BD), leading to a severe impairment in engaging a functional interaction with the others and the surrounding environment. Therefore, in ... ...

Abstract	Background: Deficits in executive functions (EF) and social cognition (SC) are often observed in bipolar disorder (BD), leading to a severe impairment in engaging a functional interaction with the others and the surrounding environment. Therefore, in recent years, resting-state functional magnetic resonance imaging (rs-fMRI) studies on BD tried to identify the neural underpinnings of these cognitive domains by exploring the association between the intrinsic functional connectivity (FC) and the scores in clinical scales evaluating these domains. Methods: A bibliographic search on PubMed and Scopus of studies evaluating the correlations between rs-fMRI findings and EF and/or SC in BD was conducted until March 2022. Ten studies met the inclusion criteria. Results: Overall, the results of the reviewed studies showed that BD patients had FC deficits compared to healthy controls (HC) in selective resting-state networks involved in EF and SC, which include the default mode network, especially the link between medial prefrontal cortex and posterior cingulate cortex, and the sensory-motor network. Finally, it also emerged the predominant role of alterations in prefrontal connections in explaining the cognitive deficits in BD patients. Limitations: The heterogeneity of the reviewed studies, in terms of the cognitive domains explored and the neuroimaging acquisitions employed, limited the comparability of the findings. Conclusions: rs-fMRI studies could help deepen the brain network alterations underlying EF and SC deficits in BD, pointing the attention on the neuronal underpinning of cognition, whose knowledge may lead to the development of new neurobiological-based approaches to improve the quality of life of these patients.
MeSH term(s)	Humans ; Bipolar Disorder ; Executive Function ; Magnetic Resonance Imaging/methods ; Quality of Life ; Social Cognition ; Brain ; Brain Mapping ; Cognition
Language	English
Publishing date	2023-03-30
Publishing country	Netherlands
Document type	Journal Article ; Review ; Research Support, Non-U.S. Gov't
ZDB-ID	135449-8
ISSN	1573-2517 ; 0165-0327
ISSN (online)	1573-2517
ISSN	0165-0327
DOI	10.1016/j.jad.2023.03.084
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Article ; Online: A systematic review on the potential use of machine learning to classify major depressive disorder from healthy controls using resting state fMRI measures.

Bondi, Elena / Maggioni, Eleonora / Brambilla, Paolo / Delvecchio, Giuseppe

Neuroscience and biobehavioral reviews

2022 Volume 144, Page(s) 104972

Abstract: Background: Major Depressive Disorder (MDD) is a psychiatric disorder characterized by functional brain deficits, as documented by resting-state functional magnetic resonance imaging (rs-fMRI) studies.: Aims: In recent years, some studies used ... ...

Abstract	Background: Major Depressive Disorder (MDD) is a psychiatric disorder characterized by functional brain deficits, as documented by resting-state functional magnetic resonance imaging (rs-fMRI) studies. Aims: In recent years, some studies used machine learning (ML) approaches, based on rs-fMRI features, for classifying MDD from healthy controls (HC). In this context, this review aims to provide a comprehensive overview of the results of these studies. Design: The studies research was performed on 3 online databases, examining English-written articles published before August 5, 2022, that performed a two-class ML classification using rs-fMRI features. The search resulted in 20 eligible studies. Results: The reviewed studies showed good performance metrics, with better performance achieved when the dataset was restricted to a more homogeneous group in terms of disease severity. Regions within the default mode network, salience network, and central executive network were reported as the most important features in the classification algorithms. Limitations: The small sample size together with the methodological and clinical heterogeneity limited the generalizability of the findings. Conclusions: In conclusion, ML applied to rs-fMRI features can be a valid approach to classify MDD and HC subjects and to discover features that can be used for additional investigation of the pathophysiology of the disease.
MeSH term(s)	Humans ; Depressive Disorder, Major ; Brain Mapping/methods ; Magnetic Resonance Imaging/methods ; Brain/diagnostic imaging ; Machine Learning
Language	English
Publishing date	2022-11-24
Publishing country	United States
Document type	Systematic Review ; Journal Article ; Review ; Research Support, Non-U.S. Gov't
ZDB-ID	282464-4
ISSN	1873-7528 ; 0149-7634
ISSN (online)	1873-7528
ISSN	0149-7634
DOI	10.1016/j.neubiorev.2022.104972
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Article ; Online: Can neuroimaging-based biomarkers predict response to cognitive remediation in patients with psychosis? A state-of-the-art review.

Biagianti, Bruno / Bigoni, Davide / Maggioni, Eleonora / Brambilla, Paolo

Journal of affective disorders

2022 Volume 305, Page(s) 196–205

Abstract: Background: Cognitive Remediation (CR) is designed to halt the pathological neural systems that characterize major psychotic disorders (MPD), and its main objective is to improve cognitive functioning. The magnitude of CR-induced cognitive gains greatly ...

Abstract	Background: Cognitive Remediation (CR) is designed to halt the pathological neural systems that characterize major psychotic disorders (MPD), and its main objective is to improve cognitive functioning. The magnitude of CR-induced cognitive gains greatly varies across patients with MPD, with up to 40% of patients not showing gains in global cognitive performance. This is likely due to the high degree of heterogeneity in neural activation patterns underlying cognitive endophenotypes, and to inter-individual differences in neuroplastic potential, cortical organization and interaction between brain systems in response to learning. Here, we review studies that used neuroimaging to investigate which biomarkers could potentially serve as predictors of treatment response to CR in MPD. Methods: This systematic review followed the PRISMA guidelines. An electronic database search (Embase, Elsevier; Scopus, PsycINFO, APA; PubMed, APA) was conducted in March 2021. peer-reviewed, English-language studies were included if they reported data for adults aged 18+ with MPD, reported findings from randomized controlled trials or single-arm trials of CR; and presented neuroimaging data. Results: Sixteen studies were included and eight neuroimaging-based biomarkers were identified. Auditory mismatch negativity (3 studies), auditory steady-state response (1), gray matter morphology (3), white matter microstructure (1), and task-based fMRI (7) can predict response to CR. Efference copy corollary/discharge, resting state, and thalamo-cortical connectivity (1) require further research prior to being implemented. Conclusions: Translational research on neuroimaging-based biomarkers can help elucidate the mechanisms by which CR influences the brain's functional architecture, better characterize psychotic subpopulations, and ultimately deliver CR that is optimized and personalized.
MeSH term(s)	Adult ; Biomarkers ; Cognition ; Cognitive Remediation/methods ; Humans ; Neuroimaging ; Psychotic Disorders/diagnostic imaging ; Psychotic Disorders/therapy
Chemical Substances	Biomarkers
Language	English
Publishing date	2022-03-10
Publishing country	Netherlands
Document type	Journal Article ; Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov't ; Review ; Systematic Review
ZDB-ID	135449-8
ISSN	1573-2517 ; 0165-0327
ISSN (online)	1573-2517
ISSN	0165-0327
DOI	10.1016/j.jad.2022.03.006
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Article ; Online: Machine learning methods to predict outcomes of pharmacological treatment in psychosis.

Del Fabro, Lorenzo / Bondi, Elena / Serio, Francesca / Maggioni, Eleonora / D'Agostino, Armando / Brambilla, Paolo

Translational psychiatry

2023 Volume 13, Issue 1, Page(s) 75

Abstract: In recent years, machine learning (ML) has been a promising approach in the research of treatment outcome prediction in psychosis. In this study, we reviewed ML studies using different neuroimaging, neurophysiological, genetic, and clinical features to ... ...

Abstract	In recent years, machine learning (ML) has been a promising approach in the research of treatment outcome prediction in psychosis. In this study, we reviewed ML studies using different neuroimaging, neurophysiological, genetic, and clinical features to predict antipsychotic treatment outcomes in patients at different stages of schizophrenia. Literature available on PubMed until March 2022 was reviewed. Overall, 28 studies were included, among them 23 using a single-modality approach and 5 combining data from multiple modalities. The majority of included studies considered structural and functional neuroimaging biomarkers as predictive features used in ML models. Specifically, functional magnetic resonance imaging (fMRI) features contributed to antipsychotic treatment response prediction of psychosis with good accuracies. Additionally, several studies found that ML models based on clinical features might present adequate predictive ability. Importantly, by examining the additive effects of combining features, the predictive value might be improved by applying multimodal ML approaches. However, most of the included studies presented several limitations, such as small sample sizes and a lack of replication tests. Moreover, considerable clinical and analytical heterogeneity among included studies posed a challenge in synthesizing findings and generating robust overall conclusions. Despite the complexity and heterogeneity of methodology, prognostic features, clinical presentation, and treatment approaches, studies included in this review suggest that ML tools may have the potential to predict treatment outcomes of psychosis accurately. Future studies need to focus on refining feature characterization, validating prediction models, and evaluate their translation in real-world clinical practice.
MeSH term(s)	Humans ; Antipsychotic Agents/therapeutic use ; Psychotic Disorders/diagnostic imaging ; Psychotic Disorders/drug therapy ; Functional Neuroimaging ; Machine Learning ; Neuroimaging
Chemical Substances	Antipsychotic Agents
Language	English
Publishing date	2023-03-02
Publishing country	United States
Document type	Journal Article ; Review ; Research Support, Non-U.S. Gov't
ZDB-ID	2609311-X
ISSN	2158-3188 ; 2158-3188
ISSN (online)	2158-3188
ISSN	2158-3188
DOI	10.1038/s41398-023-02371-z
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Article ; Online: Sustained attention alterations in major depressive disorder: A review of fMRI studies employing Go/No-Go and CPT tasks.

Piani, Maria Chiara / Maggioni, Eleonora / Delvecchio, Giuseppe / Brambilla, Paolo

Journal of affective disorders

2022 Volume 303, Page(s) 98–113

Abstract: Background: Major depressive disorder (MDD) is a severe psychiatric condition characterized by selective cognitive dysfunctions. In this regard, functional Magnetic Resonance Imaging (fMRI) studies showed, both at resting state and during tasks, ... ...

Abstract	Background: Major depressive disorder (MDD) is a severe psychiatric condition characterized by selective cognitive dysfunctions. In this regard, functional Magnetic Resonance Imaging (fMRI) studies showed, both at resting state and during tasks, alterations in the brain functional networks involved in cognitive processes in MDD patients compared to controls. Among those, it seems that the attention network may have a role in the disease pathophysiology. Therefore, in this review we aim at summarizing the current fMRI evidence investigating sustained attention in MDD patients. Methods: We conducted a search on PubMed on case-control studies on MDD employing fMRI acquisitions during Go/No-Go and continuous performance tasks. A total of 12 studies have been included in the review. Results: Overall, the majority of fMRI studies reported quantitative alterations in the response to attentive tasks in selective brain regions, including the prefrontal cortex, the cingulate cortex, the temporal and parietal lobes, the insula and the precuneus, which are key nodes of the attention, the executive, and the default mode networks. Limitations: The heterogeneity in the study designs, fMRI acquisition techniques and processing methods have limited the generalizability of the results. Conclusions: The results from the included studies showed the presence of alterations in the activation patterns of regions involved in sustained attention in MDD, which are in line with current evidence and seemed to explain some of the key symptoms of depression. However, given the paucity and heterogeneity of studies available, it may be worthwhile to continue investigating the attentional domain in MDD with ad-hoc study designs to retrieve more robust evidence.
MeSH term(s)	Brain ; Brain Mapping ; Depressive Disorder, Major ; Gyrus Cinguli ; Humans ; Magnetic Resonance Imaging
Language	English
Publishing date	2022-02-06
Publishing country	Netherlands
Document type	Journal Article ; Research Support, Non-U.S. Gov't ; Review
ZDB-ID	135449-8
ISSN	1573-2517 ; 0165-0327
ISSN (online)	1573-2517
ISSN	0165-0327
DOI	10.1016/j.jad.2022.02.003
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Article ; Online: Transcranial magnetic stimulation in major depressive disorder: Response modulation and state dependency.

Schiena, Giandomenico / Maggioni, Eleonora / Pozzoli, Sara / Brambilla, Paolo

Journal of affective disorders

2020 Volume 266, Page(s) 793–801

Abstract: Background: Transcranial Magnetic Stimulation (TMS) has emerged as a valid therapeutic option in the treatment of depression, especially in cases of inadequate response to antidepressant agents. Despite the recognized efficacy of this technique, its ... ...

Abstract	Background: Transcranial Magnetic Stimulation (TMS) has emerged as a valid therapeutic option in the treatment of depression, especially in cases of inadequate response to antidepressant agents. Despite the recognized efficacy of this technique, its mechanisms of action are still debated and optimal protocols have not yet been established. Methods: The present review focuses on TMS protocols that either engage the targeted brain circuits or synchronize the stimulation frequency to individual neuronal oscillations to increase the antidepressant efficacy. Results: TMS efficacy was found to be enhanced by preliminary or concomitant modulation of the functional state of the targeted brain networks. Conversely, there is not enough evidence of higher efficacy of TMS protocols with individual selection of the stimulation frequency compared to standard ones. Limitations: Most studies included small patient samples. Conclusions: Our results suggest that a good option to enhance rTMS efficacy might be to follow synaptic potentiation and depression rules.
MeSH term(s)	Antidepressive Agents/therapeutic use ; Brain ; Depressive Disorder, Major/drug therapy ; Humans ; Transcranial Magnetic Stimulation ; Treatment Outcome
Chemical Substances	Antidepressive Agents
Language	English
Publishing date	2020-02-04
Publishing country	Netherlands
Document type	Journal Article ; Research Support, Non-U.S. Gov't ; Review
ZDB-ID	135449-8
ISSN	1573-2517 ; 0165-0327
ISSN (online)	1573-2517
ISSN	0165-0327
DOI	10.1016/j.jad.2020.02.006
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Article: Case Report: Repeated Transcranial Magnetic Stimulation Improves Comorbid Binge Eating Disorder in Two Female Patients With Treatment-Resistant Bipolar Depression.

Sciortino, Domenico / Schiena, Giandomenico / Cantù, Filippo / Maggioni, Eleonora / Brambilla, Paolo

Frontiers in psychiatry

2021 Volume 12, Page(s) 732066

Abstract: Introduction: ...

Abstract	Introduction:
Language	English
Publishing date	2021-12-09
Publishing country	Switzerland
Document type	Case Reports
ZDB-ID	2564218-2
ISSN	1664-0640
ISSN	1664-0640
DOI	10.3389/fpsyt.2021.732066
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Article ; Online: Ultra-high field imaging in Major Depressive Disorder: a review of structural and functional studies.

Cattarinussi, Giulia / Delvecchio, Giuseppe / Maggioni, Eleonora / Bressi, Cinzia / Brambilla, Paolo

Journal of affective disorders

2021 Volume 290, Page(s) 65–73

Abstract: Background: Major depressive disorder (MDD) is a severe and pervasive psychiatric condition with a lifetime prevalence of 15-25%. Numerous Magnetic Resonance Imaging (MRI) studies employing scans at field strengths of 1.5T or 3T have been carried out in ...

Abstract	Background: Major depressive disorder (MDD) is a severe and pervasive psychiatric condition with a lifetime prevalence of 15-25%. Numerous Magnetic Resonance Imaging (MRI) studies employing scans at field strengths of 1.5T or 3T have been carried out in the last decades, providing an unprecedented insight into the neural correlates of MDD. However, in recent years, MRI technology has largely progressed and the use of scans at ultra-high field (≥ 7T) has improved the sensitivity and the resolution of MR images. In this context, with this review we aim to summarize evidence of structural and functional brain mechanisms underlying MDD obtained with ultra-high field MRI. Methods: We conducted a search on PubMed, Scopus and Web of Science of neuroimaging studies on MDD patients, which employed ultra-high field MRI. We detected six structural MRI studies, two Diffusion Tensor Imaging (DTI) studies and five functional MRI (fMRI) studies. Results: Overall, the MRI and DTI studies showed volumetric and structural connectivity alterations in the hippocampus and, to a lesser extent, in the amygdala. In contrast, more heterogeneous results were reported by fMRI studies, which, though, described functional abnormalities in the cingulate cortex, thalamus and several other brain areas. Limitations: The small sample size and the heterogeneity in patients' samples, processing and study design limit the conclusion of the present review. Conclusions: Studies employing scans at ultra-high magnetic field may provide a useful contribution to the mixed body of literature on MDD. This preliminary but promising evidence confirms the importance of performing ultra-high field MRI investigations in order to detect and better characterize subtle brain abnormalities in MDD.
MeSH term(s)	Brain/diagnostic imaging ; Depressive Disorder, Major/diagnostic imaging ; Diffusion Tensor Imaging ; Humans ; Magnetic Resonance Imaging ; Neuroimaging
Language	English
Publishing date	2021-05-02
Publishing country	Netherlands
Document type	Journal Article ; Research Support, Non-U.S. Gov't ; Review
ZDB-ID	135449-8
ISSN	1573-2517 ; 0165-0327
ISSN (online)	1573-2517
ISSN	0165-0327
DOI	10.1016/j.jad.2021.04.056
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Article ; Online: Resting-state functional connectivity and spontaneous brain activity in early-onset bipolar disorder: A review of functional Magnetic Resonance Imaging studies.

Cattarinussi, Giulia / Bellani, Marcella / Maggioni, Eleonora / Sambataro, Fabio / Brambilla, Paolo / Delvecchio, Giuseppe

Journal of affective disorders

2022 Volume 311, Page(s) 463–471

Abstract: Background: Early-onset bipolar disorder (BD) is a complex psychiatric illness characterized by mood swings, irritability and functional impairments. To improve our understanding of the pathophysiology of the disorder, we collected the existing resting- ... ...

Abstract	Background: Early-onset bipolar disorder (BD) is a complex psychiatric illness characterized by mood swings, irritability and functional impairments. To improve our understanding of the pathophysiology of the disorder, we collected the existing resting-state functional Magnetic Resonance Imaging (rs-fMRI) studies exploring resting-state functional connectivity (rs-FC) and spontaneous activity alterations in children and adolescents with BD. Methods: A search on PubMed, Web of Science and Scopus was conducted to identify all the relevant rs-fMRI investigations conducted in early-onset BD. A total of 14 studies employing different methodological approaches to explore rs-FC and spontaneous activity in early-onset BD were included (independent component analysis, n = 1; seed-based analysis, n = 7; amplitude of low frequency fluctuations analysis, n = 2; regional homogeneity analysis, n = 4). Results: Overall, the studies showed abnormalities within the Default Mode Network (DMN) and between the DMN and the Salience Network (SN). Moreover, widespread alterations in rs-FC and spontaneous brain activity within and between cortico-limbic structures, involving primarily the occipital and frontal lobes, amygdala, hippocampus, insula, thalamus and striatum were also reported. Limitations: The small sample sizes, the use of medications, the presence of comorbidities and the heterogeneity in methods hamper the integration of the study findings. Conclusions: Early-onset BD seems to be characterized by selective rs-FC and spontaneous activity dysfunctions in DMN and SN as well as in the cortico-limbic and cortico-striatal circuits, which could explain the emotive and cognitive deficits observed in this disabling psychiatric illness.
MeSH term(s)	Adolescent ; Amygdala/diagnostic imaging ; Bipolar Disorder/diagnostic imaging ; Brain/diagnostic imaging ; Brain Mapping ; Child ; Frontal Lobe ; Hippocampus ; Humans ; Magnetic Resonance Imaging/methods
Language	English
Publishing date	2022-05-14
Publishing country	Netherlands
Document type	Journal Article ; Review ; Research Support, Non-U.S. Gov't
ZDB-ID	135449-8
ISSN	1573-2517 ; 0165-0327
ISSN (online)	1573-2517
ISSN	0165-0327
DOI	10.1016/j.jad.2022.05.055
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