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Article ; Online: Neuropsychiatric Symptoms and Parkinson Disease: Are We Looking Carefully Enough?

Camicioli, Richard M / Colosimo, Carlo

2023 Volume 101, Issue 12, Page(s) 503–504

MeSH term(s)	Humans ; Parkinson Disease/diagnosis ; Mental Disorders/etiology ; Mental Disorders/diagnosis
Language	English
Publishing date	2023-07-31
Publishing country	United States
Document type	Editorial ; Comment
ZDB-ID	207147-2
ISSN	1526-632X ; 0028-3878
ISSN (online)	1526-632X
ISSN	0028-3878
DOI	10.1212/WNL.0000000000207722
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Article ; Online: Pathology in the Parkinson's Progression Markers Initiative; a Finale but also a start.

Camicioli, Richard / Cookson, Mark R

Parkinsonism & related disorders

2022 Volume 101, Page(s) 117–118

MeSH term(s)	Biomarkers ; Disease Progression ; Humans ; Parkinson Disease/diagnosis
Chemical Substances	Biomarkers
Language	English
Publishing date	2022-07-21
Publishing country	England
Document type	Editorial ; Comment
ZDB-ID	1311489-x
ISSN	1873-5126 ; 1353-8020
ISSN (online)	1873-5126
ISSN	1353-8020
DOI	10.1016/j.parkreldis.2022.07.015
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Zs.A 4553: Show issues			Location: Je nach Verfügbarkeit (siehe Angabe bei Bestand) bis Jg. 1994: Bestellungen von Artikeln über das Online-Bestellformular Jg. 1995 - 2021: Lesesall (2.OG) ab Jg. 2022: Lesesaal (EG)
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Article ; Online: Multi sequence average templates for aging and neurodegenerative disease populations.

Dadar, Mahsa / Camicioli, Richard / Duchesne, Simon

Scientific data

2022 Volume 9, Issue 1, Page(s) 238

Abstract: Magnetic resonance image (MRI) processing pipelines use average templates to enable standardization of individual MRIs in a common space. MNI-ICBM152 is currently used as the standard template by most MRI processing tools. However, MNI-ICBM152 represents ...

Abstract	Magnetic resonance image (MRI) processing pipelines use average templates to enable standardization of individual MRIs in a common space. MNI-ICBM152 is currently used as the standard template by most MRI processing tools. However, MNI-ICBM152 represents an average of 152 healthy young adult brains and is vastly different from brains of patients with neurodegenerative diseases. In those populations, extensive atrophy might cause inevitable registration errors when using an average template of young healthy individuals for standardization. Disease-specific templates that represent the anatomical characteristics of the populations can reduce such errors and improve downstream driven estimates. We present multi-sequence average templates for Alzheimer's Dementia (AD), Fronto-temporal Dementia (FTD), Lewy Body Dementia (LBD), Mild Cognitive Impairment (MCI), cognitively intact and impaired Parkinson's Disease patients (PD-CIE and PD-CI, respectively), individuals with Subjective Cognitive Impairment (SCI), AD with vascular contribution (V-AD), Vascular Mild Cognitive Impairment (V-MCI), Cognitively Intact Elderly (CIE) individuals, and a human phantom. We also provide separate templates for males and females to allow better representation of the diseases in each sex group.
MeSH term(s)	Aged ; Aging ; Alzheimer Disease/diagnostic imaging ; Cognitive Dysfunction/diagnostic imaging ; Female ; Humans ; Male ; Neurodegenerative Diseases/diagnostic imaging ; Neuropsychological Tests
Language	English
Publishing date	2022-05-27
Publishing country	England
Document type	Dataset ; Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	2775191-0
ISSN	2052-4463 ; 2052-4463
ISSN (online)	2052-4463
ISSN	2052-4463
DOI	10.1038/s41597-022-01341-2
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Article: PET Imaging in Dementia: Mini-Review and Canadian Perspective for Clinical Use.

Juengling, Freimut / Wuest, Frank / Schirrmacher, Ralf / Abele, Jonathan / Thiel, Alexander / Soucy, Jean-Paul / Camicioli, Richard / Garibotto, Valentina

The Canadian journal of neurological sciences. Le journal canadien des sciences neurologiques

2024 , Page(s) 1–13

Abstract: PET imaging is increasingly recognized as an important diagnostic tool to investigate patients with cognitive disturbances of possible neurodegenerative origin. PET with 2-[ ...

Abstract	PET imaging is increasingly recognized as an important diagnostic tool to investigate patients with cognitive disturbances of possible neurodegenerative origin. PET with 2-[
Language	English
Publishing date	2024-03-04
Publishing country	England
Document type	Journal Article ; Review
ZDB-ID	197622-9
ISSN	0317-1671
ISSN	0317-1671
DOI	10.1017/cjn.2024.31
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Article ; Online: A multi-center distributed learning approach for Parkinson's disease classification using the traveling model paradigm.

Souza, Raissa / Stanley, Emma A M / Camacho, Milton / Camicioli, Richard / Monchi, Oury / Ismail, Zahinoor / Wilms, Matthias / Forkert, Nils D

Frontiers in artificial intelligence

2024 Volume 7, Page(s) 1301997

Abstract: Distributed learning is a promising alternative to central learning for machine learning (ML) model training, overcoming data-sharing problems in healthcare. Previous studies exploring federated learning (FL) or the traveling model (TM) setup for medical ...

Abstract	Distributed learning is a promising alternative to central learning for machine learning (ML) model training, overcoming data-sharing problems in healthcare. Previous studies exploring federated learning (FL) or the traveling model (TM) setup for medical image-based disease classification often relied on large databases with a limited number of centers or simulated artificial centers, raising doubts about real-world applicability. This study develops and evaluates a convolution neural network (CNN) for Parkinson's disease classification using data acquired by 83 diverse real centers around the world, mostly contributing small training samples. Our approach specifically makes use of the TM setup, which has proven effective in scenarios with limited data availability but has never been used for image-based disease classification. Our findings reveal that TM is effective for training CNN models, even in complex real-world scenarios with variable data distributions. After sufficient training cycles, the TM-trained CNN matches or slightly surpasses the performance of the centrally trained counterpart (AUROC of 83% vs. 80%). Our study highlights, for the first time, the effectiveness of TM in 3D medical image classification, especially in scenarios with limited training samples and heterogeneous distributed data. These insights are relevant for situations where ML models are supposed to be trained using data from small or remote medical centers, and rare diseases with sparse cases. The simplicity of this approach enables a broad application to many deep learning tasks, enhancing its clinical utility across various contexts and medical facilities.
Language	English
Publishing date	2024-02-07
Publishing country	Switzerland
Document type	Journal Article
ISSN	2624-8212
ISSN (online)	2624-8212
DOI	10.3389/frai.2024.1301997
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Article: Exploiting macro- and micro-structural brain changes for improved Parkinson's disease classification from MRI data.

Camacho, Milton / Wilms, Matthias / Almgren, Hannes / Amador, Kimberly / Camicioli, Richard / Ismail, Zahinoor / Monchi, Oury / Forkert, Nils D

NPJ Parkinson's disease

2024 Volume 10, Issue 1, Page(s) 43

Abstract: Parkinson's disease (PD) is the second most common neurodegenerative disease. Accurate PD diagnosis is crucial for effective treatment and prognosis but can be challenging, especially at early disease stages. This study aimed to develop and evaluate an ... ...

Abstract	Parkinson's disease (PD) is the second most common neurodegenerative disease. Accurate PD diagnosis is crucial for effective treatment and prognosis but can be challenging, especially at early disease stages. This study aimed to develop and evaluate an explainable deep learning model for PD classification from multimodal neuroimaging data. The model was trained using one of the largest collections of T1-weighted and diffusion-tensor magnetic resonance imaging (MRI) datasets. A total of 1264 datasets from eight different studies were collected, including 611 PD patients and 653 healthy controls (HC). These datasets were pre-processed and non-linearly registered to the MNI PD25 atlas. Six imaging maps describing the macro- and micro-structural integrity of brain tissues complemented with age and sex parameters were used to train a convolutional neural network (CNN) to classify PD/HC subjects. Explainability of the model's decision-making was achieved using SmoothGrad saliency maps, highlighting important brain regions. The CNN was trained using a 75%/10%/15% train/validation/test split stratified by diagnosis, sex, age, and study, achieving a ROC-AUC of 0.89, accuracy of 80.8%, specificity of 82.4%, and sensitivity of 79.1% on the test set. Saliency maps revealed that diffusion tensor imaging data, especially fractional anisotropy, was more important for the classification than T1-weighted data, highlighting subcortical regions such as the brainstem, thalamus, amygdala, hippocampus, and cortical areas. The proposed model, trained on a large multimodal MRI database, can classify PD patients and HC subjects with high accuracy and clinically reasonable explanations, suggesting that micro-structural brain changes play an essential role in the disease course.
Language	English
Publishing date	2024-02-26
Publishing country	United States
Document type	Journal Article
ZDB-ID	2819218-7
ISSN	2373-8057
ISSN	2373-8057
DOI	10.1038/s41531-024-00647-9
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Article ; Online: Is white matter hyperintensity burden associated with cognitive and motor impairment in patients with parkinson's disease? A systematic review and meta-analysis.

Carvalho de Abreu, Daniela Cristina / Pieruccini-Faria, Frederico / Son, Surim / Montero-Odasso, Manuel / Camicioli, Richard

Neuroscience and biobehavioral reviews

2024 Volume 161, Page(s) 105677

Abstract: White matter damage quantified as white matter hyperintensities (WMH) may aggravate cognitive and motor impairments, but whether and how WMH burden impacts these problems in Parkinson's disease (PD) is not fully understood. This study aimed to examine ... ...

Abstract	White matter damage quantified as white matter hyperintensities (WMH) may aggravate cognitive and motor impairments, but whether and how WMH burden impacts these problems in Parkinson's disease (PD) is not fully understood. This study aimed to examine the association between WMH and cognitive and motor performance in PD through a systematic review and meta-analysis. We compared the WMH burden across the cognitive spectrum (cognitively normal, mild cognitive impairment, dementia) in PD including controls. Motor signs were compared in PD with low/negative and high/positive WMH burden. We compared baseline WMH burden of PD who did and did not convert to MCI or dementia. MEDLINE and EMBASE databases were used to conduct the literature search resulting in 50 studies included for data extraction. Increased WMH burden was found in individuals with PD compared with individuals without PD (i.e. control) and across the cognitive spectrum in PD (i.e. PD, PD-MCI, PDD). Individuals with PD with high/positive WMH burden had worse global cognition, executive function, and attention. Similarly, PD with high/positive WMH presented worse motor signs compared with individuals presenting low/negative WMH burden. Only three longitudinal studies were retrieved from our search and they showed that PD who converted to MCI or dementia, did not have significantly higher WMH burden at baseline, although no data was provided on WMH burden changes during the follow up. We conclude, based on cross-sectional studies, that WMH burden appears to increase with PD worse cognitive and motor status in PD.
Language	English
Publishing date	2024-04-16
Publishing country	United States
Document type	Journal Article ; Review
ZDB-ID	282464-4
ISSN	1873-7528 ; 0149-7634
ISSN (online)	1873-7528
ISSN	0149-7634
DOI	10.1016/j.neubiorev.2024.105677
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Zs.A 1371: Show issues

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Article ; Online: Identifying Biases in a Multicenter MRI Database for Parkinson's Disease Classification: Is the Disease Classifier a Secret Site Classifier?

Souza, Raissa / Winder, Anthony / Stanley, Emma A M / Vigneshwaran, Vibujithan / Camacho, Milton / Camicioli, Richard / Monchi, Oury / Wilms, Matthias / Forkert, Nils D

IEEE journal of biomedical and health informatics

2024 Volume 28, Issue 4, Page(s) 2047–2054

Abstract: Sharing multicenter imaging datasets can be advantageous to increase data diversity and size but may lead to spurious correlations between site-related biological and non-biological image features and target labels, which machine learning (ML) models may ...

Abstract	Sharing multicenter imaging datasets can be advantageous to increase data diversity and size but may lead to spurious correlations between site-related biological and non-biological image features and target labels, which machine learning (ML) models may exploit as shortcuts. To date, studies analyzing how and if deep learning models may use such effects as a shortcut are scarce. Thus, the aim of this work was to investigate if site-related effects are encoded in the feature space of an established deep learning model designed for Parkinson's disease (PD) classification based on T1-weighted MRI datasets. Therefore, all layers of the PD classifier were frozen, except for the last layer of the network, which was replaced by a linear layer that was exclusively re-trained to predict three potential bias types (biological sex, scanner type, and originating site). Our findings based on a large database consisting of 1880 MRI scans collected across 41 centers show that the feature space of the established PD model (74% accuracy) can be used to classify sex (75% accuracy), scanner type (79% accuracy), and site location (71% accuracy) with high accuracies despite this information never being explicitly provided to the PD model during original training. Overall, the results of this study suggest that trained image-based classifiers may use unwanted shortcuts that are not meaningful for the actual clinical task at hand. This finding may explain why many image-based deep learning models do not perform well when applied to data from centers not contributing to the training set.
MeSH term(s)	Humans ; Parkinson Disease/diagnostic imaging ; Magnetic Resonance Imaging/methods ; Machine Learning ; Support Vector Machine
Language	English
Publishing date	2024-04-04
Publishing country	United States
Document type	Multicenter Study ; Journal Article
ZDB-ID	2695320-1
ISSN	2168-2208 ; 2168-2194
ISSN (online)	2168-2208
ISSN	2168-2194
DOI	10.1109/JBHI.2024.3352513
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Article ; Online: Comment: Brain amyloid increases the risk of falls.

Camicioli, Richard

Neurology

2013 Volume 81, Issue 5, Page(s) 441

MeSH term(s)	Accidental Falls ; Activities of Daily Living/psychology ; Alzheimer Disease/diagnosis ; Alzheimer Disease/psychology ; Female ; Humans ; Male
Language	English
Publishing date	2013-07-30
Publishing country	United States
Document type	Comment ; Journal Article
ZDB-ID	207147-2
ISSN	1526-632X ; 0028-3878
ISSN (online)	1526-632X
ISSN	0028-3878
DOI	10.1212/WNL.0b013e31829d87aa
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Article ; Online: Effects of race, baseline cognition, and APOE on the association of affective dysregulation with incident dementia: A longitudinal study of dementia-free older adults.

Ebrahim, Inaara M / Ghahremani, Maryam / Camicioli, Richard / Smith, Eric E / Ismail, Zahinoor

Journal of affective disorders

2023 Volume 332, Page(s) 9–18

Abstract: Background: Affective symptoms are dementia risk factors. Mild behavioral impairment (MBI) is a neurobehavioral syndrome that refines incorporation of psychiatric symptomatology into dementia prognostication by stipulating symptoms must emerge de novo ... ...

Abstract	Background: Affective symptoms are dementia risk factors. Mild behavioral impairment (MBI) is a neurobehavioral syndrome that refines incorporation of psychiatric symptomatology into dementia prognostication by stipulating symptoms must emerge de novo in later life and persist for ≥6 months. Here, we investigated the longitudinal association of MBI-affective dysregulation with incident dementia. Methods: National Alzheimer Coordinating Centre participants with normal cognition (NC) or mild cognitive impairment (MCI) were included. MBI-affective dysregulation was operationalized as Neuropsychiatric Inventory Questionnaire-measured depression, anxiety, and elation at two consecutive visits. Comparators had no neuropsychiatric symptoms (no NPS) in advance of dementia. Cox proportional hazard models were implemented to assess the risk of dementia, adjusted for age, sex, years of education, race, cognitive diagnosis, and APOE-ε4 status, with interaction terms as appropriate. Results: The final sample included 3698 no-NPS (age:72.8; 62.7 % female), and 1286 MBI-affective dysregulation participants (age:75; 54.5 % female). MBI-affective dysregulation had lower dementia-free survival (p < 0.0001) and greater incidence of dementia (HR = 1.76, CI:1.48-2.08, p < 0.001) versus no NPS. Interaction analyses revealed that MBI-affective dysregulation was associated with higher dementia incidence in Black participants than White (HR = 1.70, CI:1.00-2.87, p = 0.046), NC than MCI (HR = 1.73, CI:1.21-2.48, p = 0.0028), and APOE-ε4 noncarriers than carriers (HR = 1.47, CI:1.06-2.02, p = 0.0195). Of MBI-affective dysregulation converters to dementia, 85.5 % developed Alzheimer's disease, which increased to 91.4 % in those with amnestic MCI. Limitations: MBI-affective dysregulation was not stratified by symptom to further examine dementia risk. Conclusions: Emergent and persistent affective dysregulation in dementia-free older adults is associated with substantial risk for dementia and should be considered in clinical assessments.
MeSH term(s)	Aged ; Female ; Humans ; Male ; Alzheimer Disease/epidemiology ; Alzheimer Disease/genetics ; Alzheimer Disease/complications ; Apolipoproteins E ; Cognition ; Cognitive Dysfunction/epidemiology ; Cognitive Dysfunction/genetics ; Cognitive Dysfunction/diagnosis ; Longitudinal Studies ; Neuropsychological Tests
Chemical Substances	Apolipoproteins E
Language	English
Publishing date	2023-03-28
Publishing country	Netherlands
Document type	Journal Article ; Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov't
ZDB-ID	135449-8
ISSN	1573-2517 ; 0165-0327
ISSN (online)	1573-2517
ISSN	0165-0327
DOI	10.1016/j.jad.2023.03.074
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