Alterations in subcortical magnetic susceptibility and disease-specific relationship with brain volume in major depressive disorder and schizophrenia DOI Creative Commons
Shinsuke Koike, Shuhei Shibukawa, Hirohito Kan

и другие.

Research Square (Research Square), Год журнала: 2023, Номер unknown

Опубликована: Авг. 4, 2023

Abstract Quantitative susceptibility mapping is a magnetic resonance imaging technique that measures brain tissues’ susceptibility, including iron deposition and myelination. This study examines the relationship between subcortical volume determines specific differences in these among patients with major depressive disorder (MDD), schizophrenia, healthy controls (HCs). Sex- age- matched MDD (n = 49), schizophrenia 24), HCs 50) were included. Magnetic was conducted using quantitative T1-weighted to measure volume. The acquired measurements compared groups analyses of variance post hoc comparisons. Finally, general linear model examined susceptibility–volume relationship. Significant group-level found nucleus accumbens amygdala. Although, post-hoc indicated amygdala for group significantly higher than HC group, no significant groups. interaction but not or showed alterations patients. A observed group’s accumbens, which abnormalities myelination dopaminergic system related deposition.

Язык: Английский

Using brain structural neuroimaging measures to predict psychosis onset for individuals at clinical high-risk DOI Creative Commons

Yinghan Zhu,

Norihide Maikusa, Joaquim Raduà

и другие.

Molecular Psychiatry, Год журнала: 2024, Номер 29(5), С. 1465 - 1477

Опубликована: Фев. 9, 2024

Abstract Machine learning approaches using structural magnetic resonance imaging (sMRI) can be informative for disease classification, although their ability to predict psychosis is largely unknown. We created a model with individuals at CHR who developed later (CHR-PS+) from healthy controls (HCs) that differentiate each other. also evaluated whether we could distinguish CHR-PS+ those did not develop (CHR-PS-) and uncertain follow-up status (CHR-UNK). T1-weighted brain MRI scans 1165 (CHR-PS+, n = 144; CHR-PS-, 793; CHR-UNK, 228), 1029 HCs, were obtained 21 sites. used ComBat harmonize measures of subcortical volume, cortical thickness surface area data corrected non-linear effects age sex general additive model. ( 120) HC 799) 20 sites served as training dataset, which build classifier. The remaining samples external validation datasets evaluate classifier performance (test, independent confirmatory, group [CHR-PS- CHR-UNK] datasets). accuracy the on confirmatory was 85% 73% respectively. Regional measures-including right superior frontal, temporal, bilateral insular cortices strongly contributed classifying HC. CHR-PS- CHR-UNK more likely classified compared (classification rate HC: CHR-PS+, 30%; 73%; 80%). multisite sMRI train onset in individuals, it showed promise predicting an sample. results suggest when considering adolescent development, baseline may helpful identify prognosis. Future prospective studies are required about actually clinical settings.

Язык: Английский

Процитировано

13
Application of a Machine Learning Algorithm for Structural Brain Images in Chronic Schizophrenia to Earlier Clinical Stages of Psychosis and Autism Spectrum Disorder: A Multiprotocol Imaging Dataset Study DOI Creative Commons

Yinghan Zhu,

Hironori Nakatani,

Walid Yassin

и другие.

Schizophrenia Bulletin, Год журнала: 2022, Номер 48(3), С. 563 - 574

Опубликована: Фев. 27, 2022

Machine learning approaches using structural magnetic resonance imaging (MRI) can be informative for disease classification; however, their applicability to earlier clinical stages of psychosis and other spectra is unknown. We evaluated whether a model differentiating patients with chronic schizophrenia (ChSZ) from healthy controls (HCs) could applied such as first-episode (FEP), ultra-high risk (UHR), autism spectrum disorders (ASDs).Total 359 T1-weighted MRI scans, including 154 individuals (UHR, n = 37; FEP, 24; ChSZ, 93), 64 ASD, 141 HCs, were obtained three acquisition protocols. Of these, data regarding ChSZ (n 75) HC 101) two protocols used build classifier (training dataset). The remainder was evaluate the (test, independent confirmatory, group datasets). Scanner protocol effects diminished ComBat.The accuracy test confirmatory datasets 75% 76%, respectively. bilateral pallidum inferior frontal gyrus pars triangularis strongly contributed classifying ChSZ. Schizophrenia more likely classified compared ASD (classification rate ChSZ: UHR, 41%; 54%; 70%; 19%; HC, 21%).We built multiple brain images applicable samples different spectra. predictive information useful applying neuroimaging techniques differential diagnosis predicting onset earlier.

Язык: Английский

Процитировано

33
Beyond case-control study in neuroimaging for psychiatric disorders: Harmonizing and utilizing the brain images from multiple sites DOI Creative Commons
Shinsuke Koike, Saori Tanaka,

Takuya Hayashi

и другие.

Neuroscience & Biobehavioral Reviews, Год журнала: 2025, Номер 171, С. 106063 - 106063

Опубликована: Фев. 26, 2025

Язык: Английский

Процитировано

1
Alterations in subcortical magnetic susceptibility and disease-specific relationship with brain volume in major depressive disorder and schizophrenia DOI Creative Commons
Shuhei Shibukawa, Hirohito Kan,

Shiori Honda

и другие.

Translational Psychiatry, Год журнала: 2024, Номер 14(1)

Опубликована: Март 26, 2024

Abstract Quantitative susceptibility mapping is a magnetic resonance imaging technique that measures brain tissues’ susceptibility, including iron deposition and myelination. This study examines the relationship between subcortical volume determines specific differences in these among patients with major depressive disorder (MDD), schizophrenia, healthy controls (HCs). was cross-sectional study. Sex- age- matched MDD ( n = 49), schizophrenia 24), HCs 50) were included. Magnetic conducted using quantitative T1-weighted to measure volume. The acquired measurements compared groups analyses of variance post hoc comparisons. Finally, general linear model examined susceptibility–volume relationship. Significant group-level found nucleus accumbens amygdala p 0.045). Post-hoc indicated for group significantly higher than HC 0.0054, 0.0065, respectively). However, no significant groups. interaction but not or showed alterations patients. A observed group’s accumbens, which abnormalities myelination dopaminergic system related deposition.

Язык: Английский

Процитировано

4
Human Brain Magnetic Resonance Imaging Studies for Psychiatric Disorders: The Current Progress and Future Directions DOI Open Access

Jennifer Shi,

Shinsuke Koike

JMA Journal, Год журнала: 2024, Номер 7(2), С. 197 - 204

Опубликована: Янв. 1, 2024

With the prevalence of psychiatric disorders and limitations diagnostic scheme treatment options these disorders, magnetic resonance imaging (MRI) studies play a significant role in uncovering pathological basis potentially using biological markers clinical settings. The use MRI research has grown over past three decades, current continues to provide an avenue guide development approaches therapeutic solutions. However, shortcomings derive not only from technical (i.e., range contrasts that probes or sensors can create) but also confounding factors methodological case-control for disorders. Thus, by reviewing recent literature on we explain progress brain methodologies used study We consider growing cross-disorder methods identify shared disease-specific features across In addition, need outline healthy developmental aging changes investigate disorder difference as deviation trajectory. Although have provided us with new insights, demarcation between based definitive set pathologies remains limited. This challenge disease stratification is further complicated presence multiple different sets

Язык: Английский

Процитировано

4
A brief review of the neuroimaging modalities in schizophrenia and their scope DOI
Sagarika Ray, Amit Kumar Pal, Partha Sarathi Kundu

и другие.

Annals of Medical Science and Research, Год журнала: 2024, Номер 3(1), С. 33 - 38

Опубликована: Янв. 1, 2024

Abstract Schizophrenia is a serious mental disorder characterized by diverse symptoms, including hallucinations, delusions, and disorders in thinking, behavior cognition. Its etiology multifactorial involving genetic, environmental, developmental, neurobiological factors. Neuroimaging studies have significantly contributed to understanding the underlying neural abnormalities associated with this disorder. Reduced brain volume was observed frontal temporal lobes most using structural imaging techniques. Hypofrontality functional studies. also aids differentiating lesions causing symptoms mimicking schizophrenia. However, challenges persist due variables such as age, gender, comorbidities, therapy history, substance use, coexisting psychiatric conditions, which are often insufficiently controlled for, literature. This review article comprehensively consolidates diagnostic prognostic potential of various neuroimaging techniques

Язык: Английский

Процитировано

1
Surface area in the insula was associated with 28-month functional outcome in first-episode psychosis DOI Creative Commons
Shinsuke Koike, Mao Fujioka, Yoshihiro Satomura

и другие.

Schizophrenia, Год журнала: 2021, Номер 7(1)

Опубликована: Ноя. 29, 2021

Abstract Many studies have tested the relationship between demographic, clinical, and psychobiological measurements clinical outcomes in ultra-high risk for psychosis (UHR) first-episode (FEP). However, no study has investigated multi-modal long-term >2 years. Thirty-eight individuals with UHR 29 patients FEP were measured using one or more modalities (cognitive battery, electrophysiological response, structural magnetic resonance imaging, functional near-infrared spectroscopy). We explored characteristics associated 13- 28-month outcomes. In UHR, cortical surface area left orbital part of inferior frontal gyrus was negatively 13-month disorganized symptoms. FEP, insula positively global social function. The are well-known brain schizophrenia, future on pathological mechanism alteration would provide a clearer understanding disease.

Язык: Английский

Процитировано

1
Using Brain Structural Neuroimaging Measures to Predict Psychosis Onset for Individuals at Clinical High-Risk DOI Creative Commons
Shinsuke Koike,

Yinghan Zhu,

Norihide Maikusa

и другие.

Research Square (Research Square), Год журнала: 2023, Номер unknown

Опубликована: Авг. 22, 2023

Abstract Machine learning approaches using structural magnetic resonance imaging (sMRI) can be informative for disease classification, although their ability to predict psychosis is largely unknown. We created a model with individuals at CHR who developed later (CHR-PS+) from healthy controls (HCs) that differentiate each other. also evaluated whether we could distinguish CHR-PS + those did not develop (CHR-PS-) and uncertain follow-up status (CHR-UNK). T1-weighted brain MRI scans 1,165 (CHR-PS+, n = 144; CHR-PS-, 793; CHR-UNK, 228), 1,029 HCs, were obtained 21 sites. used ComBat harmonize measures of subcortical volume, cortical thickness surface area data corrected non-linear effects age sex general additive model. CHR-PS+ (n 120) HC 799) 20 sites served as training dataset, which build classifier. The remaining samples external validation datasets evaluate classifier performance (test, independent confirmatory, group [CHR-PS- CHR-UNK] datasets). accuracy the on confirmatory was 85% 73% respectively. Regional measures-includingthose right superior frontal, temporal, bilateral insular cortices strongly contributed classifying HC. CHR-PS- CHR-UNK more likely classified compared (classification rate HC: CHR-PS+, 30%; 73%; 80%). multisite sMRI train onset in individuals, it showed promise predicting an sample. results suggest when considering adolescent development, baseline may helpful identify prognosis. Future prospective studies are required about actually clinical settings.

Язык: Английский

Процитировано

0
Alterations in subcortical magnetic susceptibility and disease-specific relationship with brain volume in major depressive disorder and schizophrenia DOI Creative Commons
Shinsuke Koike, Shuhei Shibukawa, Hirohito Kan

и другие.

Research Square (Research Square), Год журнала: 2023, Номер unknown

Опубликована: Авг. 4, 2023

Abstract Quantitative susceptibility mapping is a magnetic resonance imaging technique that measures brain tissues’ susceptibility, including iron deposition and myelination. This study examines the relationship between subcortical volume determines specific differences in these among patients with major depressive disorder (MDD), schizophrenia, healthy controls (HCs). Sex- age- matched MDD (n = 49), schizophrenia 24), HCs 50) were included. Magnetic was conducted using quantitative T1-weighted to measure volume. The acquired measurements compared groups analyses of variance post hoc comparisons. Finally, general linear model examined susceptibility–volume relationship. Significant group-level found nucleus accumbens amygdala. Although, post-hoc indicated amygdala for group significantly higher than HC group, no significant groups. interaction but not or showed alterations patients. A observed group’s accumbens, which abnormalities myelination dopaminergic system related deposition.

Язык: Английский

Процитировано

0