Sulcal characteristics patterns and gyrification gradient at different stages of Anorexia Nervosa: A structural MRI evaluation

Collantoni, Enrico; Madan, Christopher R.; Meregalli, Valentina; Meneguzzo, Paolo; Marzola, Enrica; Panero, Matteo; D’Agata, Federico; Abbate-Daga, Giovanni; Tenconi, Elena; Manara, Renzo; Favaro, Angela

doi:10.1016/j.pscychresns.2021.111350

Cited by 6 publications

(3 citation statements)

References 34 publications

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“…Sulcal information has proven to be useful in the study of a wide range of conditions; for example, in Alzheimer's disease (Maciej Plocharski and Lasse Riis Østergaard, 2016; Mateos et al, 2020), Parkinson's disease (Wang et al, 2011), and anorexia (Collantoni et al, 2021; Wagner et al, 2003). Schizophrenia has a rich and well‐replicated literature establishing patterns of cortical change (Liu et al, 2020; Palaniyappan et al, 2018; Sallet et al, 2003; Zhang et al, 2012).…”

Section: Introductionmentioning

confidence: 99%

Machine learning in small sample neuroimaging studies: Novel measures for schizophrenia analysis

Jimenez‐Mesa,

Ramirez,

et al. 2024

Human Brain Mapping

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Novel features derived from imaging and artificial intelligence systems are commonly coupled to construct computer‐aided diagnosis (CAD) systems that are intended as clinical support tools or for investigation of complex biological patterns. This study used sulcal patterns from structural images of the brain as the basis for classifying patients with schizophrenia from unaffected controls. Statistical, machine learning and deep learning techniques were sequentially applied as a demonstration of how a CAD system might be comprehensively evaluated in the absence of prior empirical work or extant literature to guide development, and the availability of only small sample datasets. Sulcal features of the entire cerebral cortex were derived from 58 schizophrenia patients and 56 healthy controls. No similar CAD systems has been reported that uses sulcal features from the entire cortex. We considered all the stages in a CAD system workflow: preprocessing, feature selection and extraction, and classification. The explainable AI techniques Local Interpretable Model‐agnostic Explanations and SHapley Additive exPlanations were applied to detect the relevance of features to classification. At each stage, alternatives were compared in terms of their performance in the context of a small sample. Differentiating sulcal patterns were located in temporal and precentral areas, as well as the collateral fissure. We also verified the benefits of applying dimensionality reduction techniques and validation methods, such as resubstitution with upper bound correction, to optimize performance.

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Section: Introductionmentioning

confidence: 99%

Machine learning in small sample neuroimaging studies: Novel measures for schizophrenia analysis

Jimenez‐Mesa,

Ramirez,

et al. 2024

Human Brain Mapping

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“…Anorexia Nervosa is a severe and often relapsing psychiatric disorder that usually emerges during adolescence and is characterised by low body weight, fear of weight gain, and body image disturbances (American Psychiatric Association, 2013). To date, many studies aimed to investigate the neurobiological basis of AN by exploring the brain structure using different indices and imaging techniques (Collantoni, Madan, et al., 2021; King et al., 2018; Meneguzzo et al., 2019). The analysis of cortical morphology has sparked particular interest in this context, as its modifications appear to occur in close relation with the acute state of starvation of the disorder and to improve after successful nutritional rehabilitation (Seitz et al., 2016).…”

Section: Introductionmentioning

confidence: 99%

Intra‐individual cortical networks in Anorexia Nervosa: Evidence from a longitudinal dataset

Collantoni,

Alberti,

Dahmen

et al. 2023

Euro Eating Disorders Rev

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ObjectiveThis work investigates cortical thickness (CT) and gyrification patterns in Anorexia Nervosa (AN) before and after short‐term weight restoration using graph theory tools.Methods38 female adolescents with AN underwent structural magnetic resonance imaging scans at baseline and after ‐ on average ‐ 3.5 months following short‐term weight restoration while 53 age‐matched healthy controls (HCs) were scanned once. Graph measures were compared between groups and longitudinally within the AN group. Associations with clinical measures such as age of onset, duration of illness, BMI standard deviation score (BMI‐SDS), and longitudinal weight changes were tested via stepwise regression.ResultsCortical thickness graphs of patients with acute AN displayed lower modularity and small‐world index (SWI) than HCs. Modularity recovered after weight gain. Reduced global efficiency and SWI were observed in patients at baseline compared to HCs based on gyrification networks. Significant associations between local clustering of CT at admission and BMI‐SDS, and clustering/global efficiency of gyrification and duration of illness emerged.ConclusionsOur results indicate a shift towards less organised CT networks in patients with acute AN. After weight recovery, the disarrangement seems to be partially reduced. However, longer‐term follow‐ups are needed to determine whether cortical organizational patterns fully return to normal.

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“…Difficulties in the quantitative characterization of the cortex are mainly due to its morphological complexity, which arises from its convoluted structure (Hofman, 1991). Surface‐based morphometry techniques have been shown to be particularly useful in this context, as they allowed the description of parameters that characterize specific aspects of the cortex, such as its thickness, gyrification index (GI), sulcal width and depth (Collantoni et al, 2021; Dale et al, 1999; Fischl & Dale, 2000; Madan, 2019; Schaer et al, 2008). The usefulness of using different indices to quantitatively characterize the cortex is mainly due to their ability to offer a nonredundant description of structural features that vary differently during development and aging and that are differently affected by distinct pathological processes (Im et al, 2006; King et al, 2010; Madan & Kensinger, 2016).…”

Section: Introductionmentioning

confidence: 99%

Cortical complexity estimation using fractal dimension: A systematic review of the literature on clinical and nonclinical samples

Meregalli

Alberti

Madan

et al. 2022

Eur J of Neuroscience

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Fractal geometry has recently been proposed as a useful tool for characterizing the complexity of the brain cortex, which is likely to derive from the recurrence of sulci-gyri convolution patterns. The index used to describe the cortical complexity is called fractal dimensional (FD) and was employed by different research exploring the neurobiological correlates of distinct pathological and nonpathological conditions. This review aims to describe the literature on the application of this index, summarize the heterogeneities between studies and inform future research on this topic. Sixty-two studies were included in the systematic review. The main research lines concern neurodevelopment, aging and the neurobiology of specific psychiatric and neurological disorders.Overall, the included papers indicate that cortical complexity is likely to reduce during aging and in various pathological processes affecting the brain.Nevertheless, the high heterogeneity between studies strongly prevents the possibility of drawing conclusions. Further research considering this index besides other morphological values is needed to better clarify the role of FD in characterizing the cortical structure.

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Sulcal characteristics patterns and gyrification gradient at different stages of Anorexia Nervosa: A structural MRI evaluation

Cited by 6 publications

References 34 publications

Machine learning in small sample neuroimaging studies: Novel measures for schizophrenia analysis

Machine learning in small sample neuroimaging studies: Novel measures for schizophrenia analysis

Intra‐individual cortical networks in Anorexia Nervosa: Evidence from a longitudinal dataset

Cortical complexity estimation using fractal dimension: A systematic review of the literature on clinical and nonclinical samples

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