Functional near infrared spectroscopy for brain functional connectivity analysis: A graph theoretic approach

Akila, V.; Johnvictor, Anita Christaline

doi:10.1016/j.heliyon.2023.e15002

Cited by 3 publications

(1 citation statement)

References 24 publications

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“…The versatility of ML models is underscored by their ability to analyse various types of data, ranging from images and tabular data to sequences and molecular structures. For this reason, several papers have also been proposed for the study of brain diseases [12] involving the analysis of radiological images [13][14][15], histopathological images [16], spectroscopy [17], electroencephalography signal processing [18], clinical genomics [19], etc. [20].…”

Section: Introductionmentioning

confidence: 99%

The Clinical Relevance of Artificial Intelligence in Migraine

Torrente,

Maccora,

Prinzi

et al. 2024

Brain Sciences

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Migraine is a burdensome neurological disorder that still lacks clear and easily accessible diagnostic biomarkers. Furthermore, a straightforward pathway is hard to find for migraineurs’ management, so the search for response predictors has become urgent. Nowadays, artificial intelligence (AI) has pervaded almost every aspect of our lives, and medicine has not been missed. Its applications are nearly limitless, and the ability to use machine learning approaches has given researchers a chance to give huge amounts of data new insights. When it comes to migraine, AI may play a fundamental role, helping clinicians and patients in many ways. For example, AI-based models can increase diagnostic accuracy, especially for non-headache specialists, and may help in correctly classifying the different groups of patients. Moreover, AI models analysing brain imaging studies reveal promising results in identifying disease biomarkers. Regarding migraine management, AI applications showed value in identifying outcome measures, the best treatment choices, and therapy response prediction. In the present review, the authors introduce the various and most recent clinical applications of AI regarding migraine.

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Section: Introductionmentioning

confidence: 99%

The Clinical Relevance of Artificial Intelligence in Migraine

Torrente,

Maccora,

Prinzi

et al. 2024

Brain Sciences

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Investigating Frontoparietal Networks and Activation in Children with Mathematics Learning Difficulties: Cases with Different Deficit Profiles

Wang,

Jamaludin

2023

Preprint

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Developmental dyscalculia (DD) is a heterogenous mathematics learning difficulty, affecting approximately 4 to 7% of children. Despite its prevalence, our current understanding of the neural underpinnings of DD remains limited. This study probed DD’s neural heterogeneity through case study comparative analyses between dyscalculia-at-risk children (DR) with non-dyscalculia-at-risk (NDR) children. Utilizing functional near-infrared spectroscopy, brain data from resting states and a mathematical computation task (addition) were acquired and analysed, using Graph theory assessing brain global and nodal network indicators. By comparing DR cases’ network indicators and activation with NDR children’s data, three DR cases demonstrated lower nodal efficiency, providing insights into potential early biomarkers of DD. Moreover, the thorough investigation of single cases can offer valuable insights for devising personalized interventions for children with DD.Research HighlightsAlthough the behavioural and cognitive heterogeneity of developmental dyscalculia (DD) has been investigated, its neural heterogeneity is under-researched.Case-control design empowers researchers to probe individual idiosyncrasies, transcending the constraints imposed by summary statistics derived from group comparisons.Graph theory metrics provided insights into the topological organization of the brain areas that underpins mathematical tasks, extending researchers’ understandings of their brain activations.Three dyscalculia-at-risk cases not only demonstrated different behaviorual and neural profiles, but also showed similar neural deficits, providing insights into potential early biomarkers of DD.

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Approaches to Consciousness Assessment in Neurocritically Ill Patients

Kang,

Kim

2024

J Korean Neurol Assoc

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The majority of neurocritically ill patients have disorders of consciousness. Both level and content of consciousness should be assessed repeatedly throughout the course of patient care. In this review, we describe the definition and diagnostic criteria for disorders of consciousness, and the neurological examination and standardized neurobehavioral assessment tools as methods of assessing consciousness. Next, we will discuss neurophysiological and neuroimaging tests as tools for assessing consciousness: automated quantitative pupillometry, electroencephalogram, evoked potentials, brain imaging, functional near-infrared spectroscopy, and biomarkers. We discuss the clinical utility of each assessment tool and the importance of a multimodal approach in the diagnosis, treatment, and prognostication of disorders of consciousness.

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Functional near infrared spectroscopy for brain functional connectivity analysis: A graph theoretic approach

Cited by 3 publications

References 24 publications

The Clinical Relevance of Artificial Intelligence in Migraine

The Clinical Relevance of Artificial Intelligence in Migraine

Investigating Frontoparietal Networks and Activation in Children with Mathematics Learning Difficulties: Cases with Different Deficit Profiles

Approaches to Consciousness Assessment in Neurocritically Ill Patients

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