All roads lead to the default-mode network—global source of DMN abnormalities in major depressive disorder

A Scalabrini, B Vai, S Poletti, S Damiani… - …, 2020 - nature.com
Major depressive disorder (MDD) is a psychiatric disorder characterized by abnormal resting
state functional connectivity (rsFC) in various neural networks and especially in default …

Quantifying performance of machine learning methods for neuroimaging data

L Jollans, R Boyle, E Artiges, T Banaschewski… - NeuroImage, 2019 - Elsevier
Abstract Machine learning is increasingly being applied to neuroimaging data. However,
most machine learning algorithms have not been designed to accommodate neuroimaging …

[HTML][HTML] Data fusion based on searchlight analysis for the prediction of Alzheimer's disease

JE Arco, J Ramírez, JM Górriz, M Ruz… - Expert Systems with …, 2021 - Elsevier
In recent years, several computer-aided diagnosis (CAD) systems have been proposed for
an early identification of dementia. Although these approaches have mostly used the …

PTSD and its dissociative subtype through the lens of the insula: Anterior and posterior insula resting‐state functional connectivity and its predictive validity using …

S Harricharan, AA Nicholson, J Thome… - …, 2020 - Wiley Online Library
Individuals with post‐traumatic stress disorder (PTSD) typically experience states of reliving
and hypervigilance; however, the dissociative subtype of PTSD (PTSD+ DS) presents with …

[HTML][HTML] Classifying heterogeneous presentations of PTSD via the default mode, central executive, and salience networks with machine learning

AA Nicholson, S Harricharan, M Densmore… - NeuroImage: Clinical, 2020 - Elsevier
Intrinsic connectivity networks (ICNs), including the default mode network (DMN), the central
executive network (CEN), and the salience network (SN) have been shown to be aberrant in …

Predicting differential diagnosis between bipolar and unipolar depression with multiple kernel learning on multimodal structural neuroimaging

B Vai, L Parenti, I Bollettini, C Cara, C Verga… - European …, 2020 - Elsevier
One of the greatest challenges in providing early effective treatment in mood disorders is the
early differential diagnosis between major depression (MDD) and bipolar disorder (BD). A …

Structural features predict sexual trauma and interpersonal problems in borderline personality disorder but not in controls: A multi-voxel pattern analysis

H Dadomo, G Salvato, G Lapomarda, Z Ciftci… - Frontiers in Human …, 2022 - frontiersin.org
Child trauma plays an important role in the etiology of Bordeline Personality Disorder (BPD).
Of all traumas, sexual trauma is the most common, severe and most associated with …

Structural features related to affective instability correctly classify patients with borderline personality disorder. A supervised machine learning approach

A Grecucci, G Lapomarda, I Messina… - Frontiers in …, 2022 - frontiersin.org
Previous morphometric studies of Borderline Personality Disorder (BPD) reported
inconsistent alterations in cortical and subcortical areas. However, these studies have …

Ensembling shallow siamese architectures to assess functional asymmetry in Alzheimer's disease progression

JE Arco, A Ortiz, D Castillo-Barnes, JM Górriz… - Applied Soft …, 2023 - Elsevier
The development of methods based on artificial intelligence for the classification of medical
imaging is widespread. Given the high dimensionality of this type of images, it is imperative …

Decoding color visual working memory from EEG signals using graph convolutional neural networks

X Che, Y Zheng, X Chen, S Song, S Li - International Journal of …, 2022 - World Scientific
Color has an important role in object recognition and visual working memory (VWM).
Decoding color VWM in the human brain is helpful to understand the mechanism of visual …