ORCID Profile
0000-0003-3414-3395
Current Organisations
IT University of Copenhagen
,
Københavns Universitet
,
Université de Montréal
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Publisher: MDPI AG
Date: 12-08-2015
DOI: 10.3390/V7082835
Publisher: Cold Spring Harbor Laboratory
Date: 14-12-2021
DOI: 10.1101/2021.12.12.21267677
Abstract: Recent developments in artificial intelligence (AI) and neuroimaging offer new opportunities for improving diagnosis and prognosis of dementia. To synthesise the available literature, we performed a systematic review. We systematically reviewed primary research publications up to January 2021, using AI for neuroimaging to predict diagnosis and/or prognosis in cognitive neurodegenerative diseases. After initial screening, data from each study was extracted, including: demographic information, AI methods, neuroimaging features, and results. We found 2709 reports, with 252 eligible papers remaining following screening. Most studies relied on the Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset (n=178) with no other in idual dataset used more than 5 times. Algorithmic classifiers, such as support vector machine (SVM), were the most commonly used AI method (47%) followed by discriminative (32%) and generative (11%) classifiers. Structural MRI was used in 71% of studies with a wide range of accuracies for the diagnosis of neurodegenerative diseases and predicting prognosis. Lower accuracy was found in studies using a multi-class classifier or an external cohort as the validation group. There was improvement in accuracy when neuroimaging modalities were combined, e.g. PET and structural MRI. Only 17 papers studied non-Alzheimer’s disease dementias. The use of AI with neuroimaging for diagnosis and prognosis in dementia is a rapidly emerging field. We make a number of recommendations addressing the definition of key clinical questions, heterogeneity of AI methods, and the availability of appropriate and representative data. We anticipate that addressing these issues will enable the field to move towards meaningful clinical translation.
Publisher: Wiley
Date: 20-01-2019
DOI: 10.1111/EJN.14320
Abstract: Mentorship facilitates personal growth through pairing trainees with mentors who can share their expertise. In times of global integration, geographical proximity between mentors and mentees is relevant to a lesser degree. This has led to popularization of online mentoring programs. In this editorial, we introduce the history and architecture of the International Online Mentoring Programme organized by the Student and Postdoc Special Interest Group of the Organization for Human Brain Mapping.
Publisher: Elsevier BV
Date: 04-2020
Publisher: Center for Open Science
Date: 12-02-2021
Abstract: Brainhack is an innovative meeting format that promotes scientific collaboration and education in an open and inclusive environment. Departing from the formats of typical scientific workshops, these events are based on grassroots projects and training, and foster open and reproducible scientific practices. We describe here the multifaceted, lasting benefits of Brainhacks for in idual participants, particularly early career researchers. We further highlight the unique contributions that Brainhacks can make to the research community, contributing to scientific progress by complementing opportunities available in conventional formats.
Publisher: Elsevier BV
Date: 06-2021
Publisher: Wiley
Date: 10-08-2023
DOI: 10.1002/ALZ.13412
Abstract: Artificial intelligence (AI) and neuroimaging offer new opportunities for diagnosis and prognosis of dementia. We systematically reviewed studies reporting AI for neuroimaging in diagnosis and/or prognosis of cognitive neurodegenerative diseases. A total of 255 studies were identified. Most studies relied on the Alzheimer's Disease Neuroimaging Initiative dataset. Algorithmic classifiers were the most commonly used AI method (48%) and discriminative models performed best for differentiating Alzheimer's disease from controls. The accuracy of algorithms varied with the patient cohort, imaging modalities, and stratifiers used. Few studies performed validation in an independent cohort. The literature has several methodological limitations including lack of sufficient algorithm development descriptions and standard definitions. We make recommendations to improve model validation including addressing key clinical questions, providing sufficient description of AI methods and validating findings in independent datasets. Collaborative approaches between experts in AI and medicine will help achieve the promising potential of AI tools in practice. There has been a rapid expansion in the use of machine learning for diagnosis and prognosis in neurodegenerative disease Most studies (71%) relied on the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset with no other in idual dataset used more than five times There has been a recent rise in the use of more complex discriminative models (e.g., neural networks) that performed better than other classifiers for classification of AD vs healthy controls We make recommendations to address methodological considerations, addressing key clinical questions, and validation We also make recommendations for the field more broadly to standardize outcome measures, address gaps in the literature, and monitor sources of bias
Publisher: Wiley
Date: 08-2005
DOI: 10.1111/J.1528-1167.2005.65804.X
Abstract: Choreoacanthocytosis (ChAc) is an autosomal recessive disorder caused by mutations in VPS13A on chromosome 9q21 and characterized by neurodegeneration and red cell acanthocytosis. Seizures are not uncommon in ChAc but have not been well characterized in the literature. We report two ChAc families in which patients presented with temporal lobe epilepsy. Detailed medical and family histories were obtained. EEG, video-telemetry, brain magnetic resonance imaging (MRI) with volumetric studies of amygdala and hippoc us, as well as neuropsychological testing were performed. Blood smears were examined for acanthocytosis. Mutation analysis of VPS13A was carried out in five patients. Six patients in three sibships were initially seen with seizures. Age at seizure onset ranged from 22 to 38 years. Seizures preceded other clinical manifestations of ChAc by < or = 15 years. The epileptic aura consisted of a sensation of déjà-vu, fear, hallucinations, palpitations, or vertigo. EEG with video-telemetry showed epileptiform discharges originating either from one or both temporal lobes. Epilepsy was generally well controlled, but some patients had periods of increased seizure frequency requiring treatment with multiple antiepileptic drugs (AEDs). Both families shared a deletion of exons 70-73 of VPS13A, extending to exons 6-7 of GNA14. Temporal lobe epilepsy may be the presenting feature of ChAc and may delay its diagnosis. Epilepsy in ChAc patients represents a challenge, because seizures may at times be difficult to control, and some AEDs may worsen the involuntary movements. Mutations in VPS13A or GNA14 or both may be associated with clinical features of temporal lobe epilepsy.
Location: United Kingdom of Great Britain and Northern Ireland
No related grants have been discovered for AmanPreet Badhwar.